[ 40%] Built target trajectory_msgs_generate_messages_nodejs
[ 40%] Built target octomap_msgs_generate_messages_nodejs
[ 40%] Built target geometry_msgs_generate_messages_cpp
[ 40%] Built target trajectory_msgs_generate_messages_cpp
[ 40%] Built target shape_msgs_generate_messages_eus
[ 40%] Built target shape_msgs_generate_messages_lisp
[ 40%] Built target shape_msgs_generate_messages_nodejs
[ 40%] Built target octomap_msgs_generate_messages_eus
[ 40%] Built target object_recognition_msgs_generate_messages_cpp
[ 60%] Built target moveit_move_group_interface
[ 80%] Built target gtest
[100%] Built target test_cleanup
[100%] Built target tests
Scanning dependencies of target
_run_tests_moveit_ros_planning_interface_rostest_test_python_move_group.test
\-- run_tests.py: execute commands
/opt/ros/lunar/share/rostest/cmake/../../../bin/rostest
--pkgdir=/tmp/catkin_workspace/src/moveit/moveit_ros/planning_interface
--package=moveit_ros_planning_interface --results-filename
test_python_move_group.xml --results-base-dir
"/tmp/catkin_workspace/test_results"
/tmp/catkin_workspace/src/moveit/moveit_ros/planning_interface/test/python_move_group.test
... logging to /home/buildfarm/.ros/log/rostest-d76771cdcc13-13085.log
[ROSUNIT] Outputting test results to
/tmp/catkin_workspace/test_results/moveit_ros_planning_interface/rostest-
test_python_move_group.xml
[0m[ INFO] [1536347237.093699451]: Loading robot model 'fanuc'... [0m
[0m[ INFO] [1536347237.155420717]: Loading robot model 'fanuc'... [0m
[0m[ INFO] [1536347237.241683671]: Publishing maintained planning scene on
'monitored_planning_scene' [0m
[0m[ INFO] [1536347237.245936055]: MoveGroup debug mode is ON [0m
[96mStarting context monitors...
[0m [0m[ INFO] [1536347237.245965237]: Starting scene monitor [0m
[0m[ INFO] [1536347237.250093831]: Listening to '/planning_scene' [0m
[0m[ INFO] [1536347237.250116708]: Starting world geometry monitor [0m
[0m[ INFO] [1536347237.257088059]: Listening to '/collision_object' using
message notifier with target frame '/base_link ' [0m
[0m[ INFO] [1536347237.261146949]: Listening to '/planning_scene_world' for
planning scene world geometry [0m
[0m[ INFO] [1536347237.279469748]: Listening to '/attached_collision_object'


unread,


for attached collision objects [0m
[96mContext monitors started.
[0m [0m[ INFO] [1536347237.324603686]: Initializing OMPL interface using ROS
parameters [0m
[0m[ INFO] [1536347237.358682618]: Using planning interface 'OMPL' [0m
[0m[ INFO] [1536347237.362537739]: Param 'default_workspace_bounds' was not
set. Using default value: 10 [0m
[0m[ INFO] [1536347237.363549620]: Param 'start_state_max_bounds_error' was
set to 0.1 [0m
[0m[ INFO] [1536347237.364278881]: Param 'start_state_max_dt' was not set.
Using default value: 0.5 [0m
[0m[ INFO] [1536347237.365351467]: Param 'start_state_max_dt' was not set.
Using default value: 0.5 [0m
[0m[ INFO] [1536347237.366614119]: Param 'jiggle_fraction' was set to 0.05 [0m
[0m[ INFO] [1536347237.367390655]: Param 'max_sampling_attempts' was not set.
Using default value: 100 [0m
[0m[ INFO] [1536347237.367450760]: Using planning request adapter 'Add Time
Parameterization' [0m
[0m[ INFO] [1536347237.367464074]: Using planning request adapter 'Fix
Workspace Bounds' [0m
[0m[ INFO] [1536347237.367475941]: Using planning request adapter 'Fix Start
State Bounds' [0m
[0m[ INFO] [1536347237.367487047]: Using planning request adapter 'Fix Start
State In Collision' [0m
[0m[ INFO] [1536347237.367497265]: Using planning request adapter 'Fix Start
State Path Constraints' [0m
[0m[ INFO] [1536347237.380840268]: Fake controller
'fake_manipulator_controller' with joints [ joint_1 joint_2 joint_3 joint_4
joint_5 joint_6 ] [0m
[0m[ INFO] [1536347237.381384220]: Returned 1 controllers in list [0m
[0m[ INFO] [1536347237.412197732]: Trajectory execution is managing
controllers [0m
[96mLoading 'move_group/ApplyPlanningSceneService'...
[0m [96mLoading 'move_group/ClearOctomapService'...
[0m [96mLoading 'move_group/MoveGroupCartesianPathService'...
[0m [96mLoading 'move_group/MoveGroupExecuteService'...
[0m [96mLoading 'move_group/MoveGroupExecuteTrajectoryAction'...
[0m [96mLoading 'move_group/MoveGroupGetPlanningSceneService'...
[0m [96mLoading 'move_group/MoveGroupKinematicsService'...
[0m [96mLoading 'move_group/MoveGroupMoveAction'...


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[92m
You can start planning now!


resolved pattern 'catkin_workspace/test_results/**/*.xml'.
[xUnit] [INFO] - Skipping the metric tool processing.
[xUnit] [INFO] - There are errors when processing test results.
[xUnit] [INFO] - Skipping tests recording.


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Build timed out (after 120 minutes). Marking the build as failed.
Build was aborted
[WARNINGS] Skipping publisher since build result is FAILURE
[xUnit] [INFO] - Starting to record.
[xUnit] [INFO] - Processing GoogleTest-1.6


//Path to a file. OPENGL_xmesa_INCLUDE_DIR:PATH=OPENGL_xmesa_INCLUDE_DIR-
NOTFOUND //Path to a file. OPENNI2_INCLUDE_DIRS:PATH=OPENNI2_INCLUDE_DIRS-
NOTFOUND //Path to a library. OPENNI2_LIBRARY:FILEPATH=OPENNI2_LIBRARY-
NOTFOUND //Path to a file. OPENNI_INCLUDE_DIRS:PATH=/usr/include/ni //Path to
a library. OPENNI_LIBRARY:FILEPATH=/usr/lib/libOpenNI.so //The directory
containing a CMake configuration file for OpenCV.
OpenCV_DIR:PATH=/opt/ros/kinetic/share/OpenCV-3.2.0-dev //C++ compiler flags
for OpenMP parallization OpenMP_CXX_FLAGS:STRING=-fopenmp //C compiler flags
for OpenMP parallization OpenMP_C_FLAGS:STRING=-fopenmp //path to apps headers
PCL_APPS_INCLUDE_DIR:PATH=/usr/include/pcl-1.7 //path to pcl_apps library
PCL_APPS_LIBRARY:FILEPATH=PCL_APPS_LIBRARY-NOTFOUND //path to pcl_apps library
debug PCL_APPS_LIBRARY_DEBUG:FILEPATH=PCL_APPS_LIBRARY_DEBUG-NOTFOUND //path
to common headers PCL_COMMON_INCLUDE_DIR:PATH=/usr/include/pcl-1.7 //path to
pcl_common library PCL_COMMON_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-
gnu/libpcl_common.so //path to pcl_common library debug
PCL_COMMON_LIBRARY_DEBUG:FILEPATH=/usr/lib/x86_64-linux-gnu/libpcl_common.so
//The directory containing a CMake configuration file for PCL.
PCL_DIR:PATH=/usr/lib/x86_64-linux-gnu/cmake/pcl //path to features headers
PCL_FEATURES_INCLUDE_DIR:PATH=/usr/include/pcl-1.7 //path to pcl_features
library PCL_FEATURES_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-
gnu/libpcl_features.so //path to pcl_features library debug
PCL_FEATURES_LIBRARY_DEBUG:FILEPATH=/usr/lib/x86_64-linux-
gnu/libpcl_features.so //path to filters headers
PCL_FILTERS_INCLUDE_DIR:PATH=/usr/include/pcl-1.7 //path to pcl_filters
library PCL_FILTERS_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-
gnu/libpcl_filters.so //path to pcl_filters library debug
PCL_FILTERS_LIBRARY_DEBUG:FILEPATH=/usr/lib/x86_64-linux-gnu/libpcl_filters.so
//path to geometry headers PCL_GEOMETRY_INCLUDE_DIR:PATH=/usr/include/pcl-1.7
//path to in_hand_scanner headers
PCL_IN_HAND_SCANNER_INCLUDE_DIR:PATH=/usr/include/pcl-1.7 //path to io headers
PCL_IO_INCLUDE_DIR:PATH=/usr/include/pcl-1.7 //path to pcl_io library
PCL_IO_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-gnu/libpcl_io.so //path to
pcl_io library debug PCL_IO_LIBRARY_DEBUG:FILEPATH=/usr/lib/x86_64-linux-
gnu/libpcl_io.so //path to kdtree headers
PCL_KDTREE_INCLUDE_DIR:PATH=/usr/include/pcl-1.7 //path to pcl_kdtree library
PCL_KDTREE_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-gnu/libpcl_kdtree.so //path
to pcl_kdtree library debug
PCL_KDTREE_LIBRARY_DEBUG:FILEPATH=/usr/lib/x86_64-linux-gnu/libpcl_kdtree.so
//path to keypoints headers
PCL_KEYPOINTS_INCLUDE_DIR:PATH=/usr/include/pcl-1.7 //path to pcl_keypoints


See < [
http://build.ros.org/job/Ldev__moveit__ubuntu_xenial_amd64/270/display/redirect
](http://build.ros.org/job/Ldev__moveit__ubuntu_xenial_amd64/270/display/redirect)
>


See < [
http://build.ros.org/job/Ldev__moveit__ubuntu_xenial_amd64/256/display/redirect?page=changes
](http://build.ros.org/job/Ldev__moveit__ubuntu_xenial_amd64/256/display/redirect?page=changes)
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Setting up python-pyside2.qtconcurrent
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtcore
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtgui
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qthelp
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtnetwork
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtprintsupport
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtqml
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtquick
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtquickwidgets
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtscript
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtsql
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtsvg
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qttest
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtuitools
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtwebkit
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtwebkitwidgets
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtwidgets
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtx11extras
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2.qtxml
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...
Setting up python-pyside2
(2.0.0+dev-0~201604151742~rev1858~pkg38~ubuntu16.04.1) ...


Training


Aug 1, 2018, 4:13:07 AM  8/1/18


\-- Configuring done
\-- Generating done


To install the necessary MoveIt packages through terminal, type the following:


Given the demo.launch file given by MoveIt, which launches the robot model
amongst other necessities, the custom MoveIt API node, and the custom HEBI
motor control node, one would launch the nodes in this succession:


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Sep 5, 2018, 9:10:25 PM  9/5/18


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See < [
http://build.ros.org/job/Ldev__moveit__ubuntu_xenial_amd64/268/display/redirect?page=changes
](http://build.ros.org/job/Ldev__moveit__ubuntu_xenial_amd64/268/display/redirect?page=changes)
>


to mikael+b...@osrfoundation.org, ros-build...@googlegroups.com,
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jon.b...@gmail.com, jorge...@swri.org, mathias...@ipa.fraunhofer.de,
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#  [ dpiet ](/dpiet) /  **[ Console output after ros2-control package install
check ](/dpiet/e1f2ab0e66e6619390a450c280dc2985) **


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\-- program_options
\-- signals


Aug 22, 2018, 10:21:31 AM  8/22/18


[
![../../../_images/setup_assistant_panda_100.png](../../../_images/setup_assistant_panda_100.png)
](../../../_images/setup_assistant_panda_100.png)
##  Step 2: Generate Self-Collision Matrix  
The Default Self-Collision Matrix Generator searches for pairs of links on the
robot that can safely be disabled from collision checking, decreasing motion
planning processing time. These pairs of links are disabled when they are
always in collision, never in collision, in collision in the robot’s default
position or when the links are adjacent to each other on the kinematic chain.
The sampling density specifies how many random robot positions to check for
self collision. Higher densities require more computation time while lower
densities have a higher possibility of disabling pairs that should not be
disabled. The default value is 10,000 collision checks. Collision checking is
done in parallel to decrease processing time.
  * Click on the _Self-Collisions_ pane selector on the left-hand side and click on the _Generate Collision Matrix_ button. The Setup Assistant will work for a few second before presenting you the results of its computation in the main table.
[ ![before](../../../_images/setup_assistant_panda_self_collisions.png)
](../../../_images/setup_assistant_panda_self_collisions.png) → [
![after](../../../_images/setup_assistant_panda_self_collisions_done.png)
](../../../_images/setup_assistant_panda_self_collisions_done.png)
##  Step 3: Add Virtual Joints  
Virtual joints are used primarily to attach the robot to the world. For the
Panda we will define only one virtual joint attaching the _panda_link0_ of the
Panda to the _world_ world frame. This virtual joint represents the motion of
the base of the robot in a plane.
  * Click on the _Virtual Joints_ pane selector. Click on _Add Virtual Joint_
  * Set the joint name as “virtual_joint”
  * Set the child link as “panda_link0” and the parent frame name as “world”.
  * Set the Joint Type as “fixed”.
  * Click _Save_ and you should see this screen:
[
![../../../_images/setup_assistant_panda_virtual_joints.png](../../../_images/setup_assistant_panda_virtual_joints.png)
](../../../_images/setup_assistant_panda_virtual_joints.png)
##  Step 4: Add Planning Groups  
Planning groups are used for semantically describing different parts of your
robot, such as defining what an arm is, or an end effector.
  * Click on the _Planning Groups_ pane selector.
  * Click on _Add Group_ and you should see the following screen:
[
![../../../_images/setup_assistant_panda_planning_groups.png](../../../_images/setup_assistant_panda_planning_groups.png)
](../../../_images/setup_assistant_panda_planning_groups.png)


We have already added the gripper of the Panda. Now, we will designate this
group as a special group: **end effectors** . Designating this group as end
effectors allows some special operations to happen on them internally.
  * Click on the _End Effectors_ pane.
  * Click _Add End Effector_ .
  * Choose **hand** as the _End Effector Name_ for the gripper.
  * Select **hand** as the _End Effector Group_ .
  * Select **panda_link8** as the _Parent Link_ for this end-effector.
  * Leave _Parent Group_ blank.
[
![../../../_images/setup_assistant_panda_end_effector_add.png](../../../_images/setup_assistant_panda_end_effector_add.png)
](../../../_images/setup_assistant_panda_end_effector_add.png)
##  Step 7: Add Passive Joints  
The passive joints tab is meant to allow specification of any passive joints
that might exist in a robot. These are joints that are unactuated on a robot
(e.g. passive casters.) This tells the planners that they cannot
(kinematically) plan for these joints because they can’t be directly
controlled. The Panda does not have any passive joints so we will skip this
step.
##  Step 8: 3D Perception  
The 3D Perception tab is meant to set the parameters of the YAML configuration
file for configuring the 3D sensors **sensors_3d.yaml** .
e.g. **point_cloud** parameters:
[
![../../../_images/setup_assistant_panda_3d_perception_point_cloud.png](../../../_images/setup_assistant_panda_3d_perception_point_cloud.png)
](../../../_images/setup_assistant_panda_3d_perception_point_cloud.png)
For more details about those parameters please see perception pipeline [
tutorial  ](../perception_pipeline/perception_pipeline_tutorial.html)
In case of **sensors_3d.yaml** was not needed, choose **None** .
[
![../../../_images/setup_assistant_panda_3d_perception.png](../../../_images/setup_assistant_panda_3d_perception.png)
](../../../_images/setup_assistant_panda_3d_perception.png)
##  Step 9: Gazebo Simulation  
The Simulation tab can be used to help you simulate your robot with Gazebo by
generating a new Gazebo compatible urdf if needed.
[
![../../../_images/setup_assistant_panda_simulation_screen.png](../../../_images/setup_assistant_panda_simulation_screen.png)
](../../../_images/setup_assistant_panda_simulation_screen.png)
You can use the generated robot **urdf** to spawn the robot in Gazebo in the
following way.


\-- Using PYTHON_EXECUTABLE: /usr/bin/python
\-- Using Debian Python package layout
\-- Using empy: /usr/bin/empy
\-- Using CATKIN_ENABLE_TESTING: 1
\-- Call enable_testing()
\-- Using CATKIN_TEST_RESULTS_DIR: /tmp/catkin_workspace/test_results
\-- Found gmock sources under '/usr/src/gmock': gmock will be built
\-- Found gtest sources under '/usr/src/gmock': gtests will be built
\-- Using Python nosetests: /usr/bin/nosetests-2.7
\-- catkin 0.7.14
\-- Using these message generators: gencpp;geneus;genlisp;gennodejs;genpy
\-- Configuring done
\-- Generating done


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Build was aborted
[WARNINGS] Skipping publisher since build result is FAILURE
[xUnit] [INFO] - Starting to record.
[xUnit] [INFO] - Processing GoogleTest-1.6


//Value Computed by CMake
industrial_utils_SOURCE_DIR:STATIC=/home/robuter/catkin_ws/src/industrial_core/industrial_utils
//The directory containing a CMake configuration file for interactive_markers.
interactive_markers_DIR:PATH=/opt/ros/kinetic/share/interactive_markers/cmake
//The directory containing a CMake configuration file for kdl_conversions.
kdl_conversions_DIR:PATH=/opt/ros/kinetic/share/kdl_conversions/cmake //The
directory containing a CMake configuration file for kdl_parser.
kdl_parser_DIR:PATH=/opt/ros/kinetic/share/kdl_parser/cmake //The directory
containing a CMake configuration file for laser_geometry.
laser_geometry_DIR:PATH=/opt/ros/kinetic/share/laser_geometry/cmake //Path to
a library. lib:FILEPATH=/opt/ros/kinetic/lib/libkdl_conversions.so
//Dependencies for the target
libhokuyo_LIB_DEPENDS:STATIC=general;/opt/ros/kinetic/lib/librosconsole.so;general;/opt/ros/kinetic/lib/librosconsole_log4cxx.so;general;/opt/ros/kinetic/lib/librosconsole_backend_interface.so;general;/usr/lib/x86_64-linux-
gnu/liblog4cxx.so;general;/usr/lib/x86_64-linux-
gnu/libboost_regex.so;general;/opt/ros/kinetic/lib/librostime.so;general;/opt/ros/kinetic/lib/libcpp_common.so;general;/usr/lib/x86_64-linux-
gnu/libboost_system.so;general;/usr/lib/x86_64-linux-
gnu/libboost_thread.so;general;/usr/lib/x86_64-linux-
gnu/libboost_chrono.so;general;/usr/lib/x86_64-linux-
gnu/libboost_date_time.so;general;/usr/lib/x86_64-linux-
gnu/libboost_atomic.so;general;/usr/lib/x86_64-linux-
gnu/libpthread.so;general;/usr/lib/x86_64-linux-gnu/libconsole_bridge.so;
//The directory containing a CMake configuration file for map_msgs.
map_msgs_DIR:PATH=/opt/ros/kinetic/share/map_msgs/cmake //The directory
containing a CMake configuration file for message_filters.
message_filters_DIR:PATH=/opt/ros/kinetic/share/message_filters/cmake //The
directory containing a CMake configuration file for message_generation.
message_generation_DIR:PATH=/opt/ros/kinetic/share/message_generation/cmake
//The directory containing a CMake configuration file for message_runtime.
message_runtime_DIR:PATH=/opt/ros/kinetic/share/message_runtime/cmake //Value
Computed by CMake
moveit_BINARY_DIR:STATIC=/home/robuter/catkin_ws/build/moveit-kinetic-
devel/moveit //Value Computed by CMake
moveit_SOURCE_DIR:STATIC=/home/robuter/catkin_ws/src/moveit-kinetic-
devel/moveit //Dependencies for the target
moveit_background_processing_LIB_DEPENDS:STATIC=general;/opt/ros/kinetic/lib/libeigen_conversions.so;general;/opt/ros/kinetic/lib/libgeometric_shapes.so;general;/opt/ros/kinetic/lib/liboctomap.so;general;/opt/ros/kinetic/lib/liboctomath.so;general;/opt/ros/kinetic/lib/libkdl_parser.so;general;/opt/ros/kinetic/lib/liborocos-
kdl.so.1.3.0;general;/opt/ros/kinetic/lib/liburdf.so;general;/usr/lib/x86_64-linux-
gnu/liburdfdom_sensor.so;general;/usr/lib/x86_64-linux-
gnu/liburdfdom_model_state.so;general;/usr/lib/x86_64-linux-
gnu/liburdfdom_model.so;general;/usr/lib/x86_64-linux-
gnu/liburdfdom_world.so;general;/opt/ros/kinetic/lib/librosconsole_bridge.so;general;/opt/ros/kinetic/lib/libroscpp.so;general;/usr/lib/x86_64-linux-


Refer to the official MoveIt tutorials in order to get the robot up and
running using the MoveIt pipeline through ROS. It is recommended to code the
MoveIt pipeline through C++.


Oct 23, 2018, 7:43:37 PM  10/23/18


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\-- Build files have been written to:
/tmp/catkin_workspace/build_isolated/moveit_ros_move_group
==> make -j1 in '/tmp/catkin_workspace/build_isolated/moveit_ros_move_group'
[ 0%] Built target std_msgs_generate_messages_lisp
[ 0%] Built target _catkin_empty_exported_target
[ 0%] Built target visualization_msgs_generate_messages_eus
[ 0%] Built target actionlib_msgs_generate_messages_nodejs
[ 0%] Built target moveit_msgs_generate_messages_py
[ 0%] Built target moveit_msgs_generate_messages_eus
[ 0%] Built target roscpp_generate_messages_py
[ 0%] Built target roscpp_generate_messages_eus
[ 0%] Built target visualization_msgs_generate_messages_lisp
[ 0%] Built target std_msgs_generate_messages_nodejs
[ 0%] Built target visualization_msgs_generate_messages_cpp
[ 0%] Built target geometry_msgs_generate_messages_cpp
[ 0%] Built target trajectory_msgs_generate_messages_cpp
[ 0%] Built target octomap_msgs_generate_messages_nodejs
[ 0%] Built target rosgraph_msgs_generate_messages_cpp
[ 0%] Built target object_recognition_msgs_generate_messages_lisp
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From here, you can move the robot arm to your preset positions or move the arm
through various other states by dragging-and-dropping the arm into different
positions.


Once you have the model, it is time to run the MoveIt setup manager GUI. On a
terminal, type the following command (make sure to source setup.bash to use
ROS commands through terminal):


Forward


###  ROS Buildfarm


Reply to author


Moving onto “Robot Poses”, you can configure different preset positions of the
robot. In the screenshot below, preset positions were made for when the serial
manipulator arm is stretched out, compacted in, and in an intermediate
position. Create however many preset poses you desire.


\-- thread
\-- serialization
\-- chrono


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Instantly share code, notes, and snippets.


unread,


to mikael+b...@osrfoundation.org, ros-build...@googlegroups.com,
13...@2000.jukuin.keio.ac.jp, ch...@live.in, da...@dav.ee,
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jon.b...@gmail.com, jorge...@swri.org, mathias...@ipa.fraunhofer.de,
m...@v4hn.de, mfer...@fetchrobotics.com, moveit_r...@googlegroups.com,
rhas...@techfak.uni-bielefeld.de


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See < [
http://build.ros.org/job/Ldev__moveit__ubuntu_xenial_amd64/266/display/redirect
](http://build.ros.org/job/Ldev__moveit__ubuntu_xenial_amd64/266/display/redirect)
>


Delete


\-- Using PYTHON_EXECUTABLE: /usr/bin/python
\-- Using Debian Python package layout
\-- Using empy: /usr/bin/empy
\-- Using CATKIN_ENABLE_TESTING: 1
\-- Call enable_testing()
\-- Using CATKIN_TEST_RESULTS_DIR: /tmp/catkin_workspace/test_results
\-- Found gmock sources under '/usr/src/gmock': gmock will be built
\-- Found gtest sources under '/usr/src/gmock': gtests will be built
\-- Using Python nosetests: /usr/bin/nosetests-2.7
\-- catkin 0.7.14
\-- Using these message generators: gencpp;geneus;genlisp;gennodejs;genpy


* Save dpiet/e1f2ab0e66e6619390a450c280dc2985 to your computer and use it in GitHub Desktop.


-DCATKIN_DEVEL_PREFIX=/tmp/catkin_workspace/devel_isolated/moveit_fake_controller_manager
-DCMAKE_INSTALL_PREFIX=/tmp/catkin_workspace/install_isolated
-DCATKIN_ENABLE_TESTING=1 -DCATKIN_SKIP_TESTING=0
-DCATKIN_TEST_RESULTS_DIR=/tmp/catkin_workspace/test_results -G Unix Makefiles
in '/tmp/catkin_workspace/build_isolated/moveit_fake_controller_manager'


ADVANCED:INTERNAL=1 //ADVANCED property for variable: PCL_REGISTRATION_LIBRARY
PCL_REGISTRATION_LIBRARY-ADVANCED:INTERNAL=1 //ADVANCED property for variable:
PCL_REGISTRATION_LIBRARY_DEBUG PCL_REGISTRATION_LIBRARY_DEBUG-
ADVANCED:INTERNAL=1 //ADVANCED property for variable:
PCL_SAMPLE_CONSENSUS_LIBRARY PCL_SAMPLE_CONSENSUS_LIBRARY-ADVANCED:INTERNAL=1
//ADVANCED property for variable: PCL_SAMPLE_CONSENSUS_LIBRARY_DEBUG
PCL_SAMPLE_CONSENSUS_LIBRARY_DEBUG-ADVANCED:INTERNAL=1 //ADVANCED property for
variable: PCL_SEARCH_LIBRARY PCL_SEARCH_LIBRARY-ADVANCED:INTERNAL=1 //ADVANCED
property for variable: PCL_SEARCH_LIBRARY_DEBUG PCL_SEARCH_LIBRARY_DEBUG-
ADVANCED:INTERNAL=1 //ADVANCED property for variable: PCL_SEGMENTATION_LIBRARY
PCL_SEGMENTATION_LIBRARY-ADVANCED:INTERNAL=1 //ADVANCED property for variable:
PCL_SEGMENTATION_LIBRARY_DEBUG PCL_SEGMENTATION_LIBRARY_DEBUG-
ADVANCED:INTERNAL=1 //ADVANCED property for variable: PCL_SURFACE_LIBRARY
PCL_SURFACE_LIBRARY-ADVANCED:INTERNAL=1 //ADVANCED property for variable:
PCL_SURFACE_LIBRARY_DEBUG PCL_SURFACE_LIBRARY_DEBUG-ADVANCED:INTERNAL=1
//ADVANCED property for variable: PCL_TRACKING_LIBRARY PCL_TRACKING_LIBRARY-
ADVANCED:INTERNAL=1 //ADVANCED property for variable:
PCL_TRACKING_LIBRARY_DEBUG PCL_TRACKING_LIBRARY_DEBUG-ADVANCED:INTERNAL=1
//ADVANCED property for variable: PCL_VISUALIZATION_LIBRARY
PCL_VISUALIZATION_LIBRARY-ADVANCED:INTERNAL=1 //ADVANCED property for
variable: PCL_VISUALIZATION_LIBRARY_DEBUG PCL_VISUALIZATION_LIBRARY_DEBUG-
ADVANCED:INTERNAL=1 PC_EIGEN_CFLAGS:INTERNAL=-I/usr/include/eigen3
PC_EIGEN_CFLAGS_I:INTERNAL= PC_EIGEN_CFLAGS_OTHER:INTERNAL=
PC_EIGEN_FOUND:INTERNAL=1 PC_EIGEN_INCLUDEDIR:INTERNAL=
PC_EIGEN_INCLUDE_DIRS:INTERNAL=/usr/include/eigen3 PC_EIGEN_LDFLAGS:INTERNAL=
PC_EIGEN_LDFLAGS_OTHER:INTERNAL= PC_EIGEN_LIBDIR:INTERNAL=
PC_EIGEN_LIBRARIES:INTERNAL= PC_EIGEN_LIBRARY_DIRS:INTERNAL=
PC_EIGEN_LIBS:INTERNAL= PC_EIGEN_LIBS_L:INTERNAL=
PC_EIGEN_LIBS_OTHER:INTERNAL= PC_EIGEN_LIBS_PATHS:INTERNAL=
PC_EIGEN_PREFIX:INTERNAL=/usr
PC_EIGEN_STATIC_CFLAGS:INTERNAL=-I/usr/include/eigen3
PC_EIGEN_STATIC_CFLAGS_I:INTERNAL= PC_EIGEN_STATIC_CFLAGS_OTHER:INTERNAL=
PC_EIGEN_STATIC_INCLUDE_DIRS:INTERNAL=/usr/include/eigen3
PC_EIGEN_STATIC_LDFLAGS:INTERNAL= PC_EIGEN_STATIC_LDFLAGS_OTHER:INTERNAL=
PC_EIGEN_STATIC_LIBDIR:INTERNAL= PC_EIGEN_STATIC_LIBRARIES:INTERNAL=
PC_EIGEN_STATIC_LIBRARY_DIRS:INTERNAL= PC_EIGEN_STATIC_LIBS:INTERNAL=
PC_EIGEN_STATIC_LIBS_L:INTERNAL= PC_EIGEN_STATIC_LIBS_OTHER:INTERNAL=
PC_EIGEN_STATIC_LIBS_PATHS:INTERNAL= PC_EIGEN_VERSION:INTERNAL=3.2.92
PC_EIGEN_eigen3_INCLUDEDIR:INTERNAL= PC_EIGEN_eigen3_LIBDIR:INTERNAL=
PC_EIGEN_eigen3_PREFIX:INTERNAL= PC_EIGEN_eigen3_VERSION:INTERNAL=


\-- Build files have been written to:
/tmp/catkin_workspace/build_isolated/moveit_planners_chomp
==> make -j1 in '/tmp/catkin_workspace/build_isolated/moveit_planners_chomp'
Scanning dependencies of target moveit_planners_chomp
[ 20%] Building CXX object
CMakeFiles/moveit_planners_chomp.dir/src/chomp_interface.cpp.o
[ 40%] Building CXX object
CMakeFiles/moveit_planners_chomp.dir/src/chomp_planning_context.cpp.o
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Scanning dependencies of target chomp_planner_plugin
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[100%] Built target chomp_planner_plugin
<== Finished processing package [21 of 26]: 'moveit_planners_chomp'

==> Processing catkin package: 'moveit_ros_visualization'
==> Building with env:
'/tmp/catkin_workspace/devel_isolated/moveit_planners_chomp/env.sh'


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user=s40-c)


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|  [rviz2-1] [INFO] [1630341485.327271598]
[moveit_ros_visualization.motion_planning_frame_planning]: planner id:
panda_arm[BiESTkConfigDefault]
|  [rviz2-1] [INFO] [1630341485.327276102]
[moveit_ros_visualization.motion_planning_frame_planning]: planner id:
panda_arm[BiTRRTkConfigDefault]
|  [rviz2-1] [INFO] [1630341485.327280188]
[moveit_ros_visualization.motion_planning_frame_planning]: planner id:
panda_arm[ESTkConfigDefault]
|  [rviz2-1] [INFO] [1630341485.327284327]
[moveit_ros_visualization.motion_planning_frame_planning]: planner id:
panda_arm[FMTkConfigDefault]
|  [rviz2-1] [INFO] [1630341485.327287934]
[moveit_ros_visualization.motion_planning_frame_planning]: planner id:
panda_arm[KPIECEkConfigDefault]
|  [rviz2-1] [INFO] [1630341485.327291986]
[moveit_ros_visualization.motion_planning_frame_planning]: planner id:
panda_arm[LBKPIECEkConfigDefault]
|  [rviz2-1] [INFO] [1630341485.327294638]
[moveit_ros_visualization.motion_planning_frame_planning]: planner id:
panda_arm[LBTRRTkConfigDefault]
|  [rviz2-1] [INFO] [1630341485.327297410]
[moveit_ros_visualization.motion_planning_frame_planning]: planner id:
panda_arm[LazyPRMkConfigDefault]
|  [rviz2-1] [INFO] [1630341485.327300064]
[moveit_ros_visualization.motion_planning_frame_planning]: planner id:
panda_arm[LazyPRMstarkConfigDefault]
|  [rviz2-1] [INFO] [1630341485.327302737]
[moveit_ros_visualization.motion_planning_frame_planning]: planner id:
panda_arm[PDSTkConfigDefault]
|  [rviz2-1] [INFO] [1630341485.327305371]
[moveit_ros_visualization.motion_planning_frame_planning]: planner id:
panda_arm[PRMkConfigDefault]
|  [rviz2-1] [INFO] [1630341485.327308084]
[moveit_ros_visualization.motion_planning_frame_planning]: planner id:
panda_arm[PRMstarkConfigDefault]
|  [rviz2-1] [INFO] [1630341485.327311047]
[moveit_ros_visualization.motion_planning_frame_planning]: planner id:
panda_arm[ProjESTkConfigDefault]
|  [rviz2-1] [INFO] [1630341485.327313640]


Add the arm
  * We will first add Panda arm as a planning group
    * Enter _Group Name_ as **panda_arm**
    * Choose _kdl_kinematics_plugin/KDLKinematicsPlugin_ as the kinematics solver. _Note: if you have a custom robot and would like a powerful custom IK solver, see_ [ Kinematics/IKFast  ](../ikfast/ikfast_tutorial.html)
    * Let _Kin. Search Resolution_ and _Kin. Search Timeout_ stay at their default values.
[
![../../../_images/setup_assistant_panda_arm.png](../../../_images/setup_assistant_panda_arm.png)
](../../../_images/setup_assistant_panda_arm.png)
  * Now, click on the _Add Joints_ button. You will see a list of joints on the left hand side. You need to choose all the joints that belong to the arm and add them to the right hand side. The joints are arranged in the order that they are stored in an internal tree structure. This makes it easy to select a serial chain of joints.
    * Click on **virtual_joint** , hold down the **Shift** button on your keyboard and then click on the _panda_joint8_ . Now click on the ** > ** button to add these joints into the list of selected joints on the right.
[
![../../../_images/setup_assistant_panda_arm_joints.png](../../../_images/setup_assistant_panda_arm_joints.png)
](../../../_images/setup_assistant_panda_arm_joints.png)
  * Click _Save_ to save the selected group.
[
![../../../_images/setup_assistant_panda_arm_joints_saved.png](../../../_images/setup_assistant_panda_arm_joints_saved.png)
](../../../_images/setup_assistant_panda_arm_joints_saved.png)
Add the gripper
  * We will also add a group for the end effector. NOTE that you will do this using a different procedure than adding the arm.
    * Click on the _Add Group_ button.
    * Enter _Group Name_ as **hand**
    * Let _Kinematic Solver_ stay at its default value; **None** .
    * Let _Kin. Search Resolution_ and _Kin. Search Timeout_ stay at their default values.
    * Click on the _Add Links_ button.
    * Choose **panda_hand** , **panda_leftfinger** , and **panda_rightfinger** and add them to the list of _Selected Links_ on the right hand side.
    * Click _Save_
[
![../../../_images/setup_assistant_panda_planning_groups_gripper.png](../../../_images/setup_assistant_panda_planning_groups_gripper.png)
](../../../_images/setup_assistant_panda_planning_groups_gripper.png)
##  Step 5: Add Robot Poses  
The Setup Assistant allows you to add certain fixed poses into the
configuration. This helps if, for example, you want to define a certain
position of the robot as a **Home** position.
  * Click on the _Robot Poses_ pane.
  * Click _Add Pose_ . Choose a name for the pose. The robot will be in its _Default_ position where the joint values are set to the mid-range of the allowed joint value range. Move the individual joints around until you are happy and then _Save_ the pose. Note how poses are associated with particular groups. You can save individual poses for each group.
  * **IMPORTANT TIP** : Try to move all the joints around. If there is something wrong with the joint limits in your URDF, you should be able to see it immediately here.
[
![../../../_images/setup_assistant_panda_saved_poses.png](../../../_images/setup_assistant_panda_saved_poses.png)
](../../../_images/setup_assistant_panda_saved_poses.png)
##  Step 6: Label End Effectors  


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[ 62%] Building CXX object
motion_planning_rviz_plugin/CMakeFiles/moveit_motion_planning_rviz_plugin_core.dir/src/motion_planning_frame_scenes.cpp.o
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Once you have the HEBI motors installed on the robot, connect to the HEBI
motors using the Scope program provided by HEBI: [ HEBI Scope download page
](https://www.hebirobotics.com/apps)


###  ROS Buildfarm


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Forward


Aug 21, 2018, 8:12:58 AM  8/21/18


Preparing to unpack .../libqt5sql5-sqlite_5.5.1+dfsg-16ubuntu7.5_amd64.deb ...
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\-- Boost version: 1.58.0
\-- Found the following Boost libraries:


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Jul 13, 2018, 8:18:39 AM  7/13/18


[96mLoading 'move_group/ApplyPlanningSceneService'...
[0m [96mLoading 'move_group/ClearOctomapService'...
[0m [96mLoading 'move_group/MoveGroupCartesianPathService'...


Aug 23, 2018, 10:16:57 PM  8/23/18


##  Installation


containing a CMake configuration file for Qt5Gui.
Qt5Gui_DIR:PATH=/usr/lib/x86_64-linux-gnu/cmake/Qt5Gui //The directory
containing a CMake configuration file for Qt5Network.
Qt5Network_DIR:PATH=/usr/lib/x86_64-linux-gnu/cmake/Qt5Network //The directory
containing a CMake configuration file for Qt5WebKit.
Qt5WebKit_DIR:PATH=/usr/lib/x86_64-linux-gnu/cmake/Qt5WebKit //The directory
containing a CMake configuration file for Qt5Widgets.
Qt5Widgets_DIR:PATH=/usr/lib/x86_64-linux-gnu/cmake/Qt5Widgets //The directory
containing a CMake configuration file for Qt5.
Qt5_DIR:PATH=/usr/lib/x86_64-linux-gnu/cmake/Qt5 //Path to a library.
RT_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-gnu/librt.so //Enable debian style
python package layout SETUPTOOLS_DEB_LAYOUT:BOOL=ON //LSB Distrib tag
UBUNTU:BOOL=TRUE //LSB Distrib - codename tag UBUNTU_XENIAL:BOOL=TRUE //Path
to a library. YAML:FILEPATH=/usr/lib/x86_64-linux-gnu/libyaml-cpp.so //Path to
a file. _CATKIN_GTEST_INCLUDE:FILEPATH=/usr/include/gtest/gtest.h //Path to a
file. _CATKIN_GTEST_SRC:FILEPATH=/usr/src/gtest/src/gtest.cc //Path to a file.
_file_name:FILEPATH=/home/robuter/catkin_ws/src/moveit-kinetic-
devel/moveit_ros/visualization/test/moveit_joy.test //Path to a file.
_path_name:FILEPATH=/home/robuter/catkin_ws/src/image_common/camera_calibration_parsers/test/parser.py
//The directory containing a CMake configuration file for actionlib.
actionlib_DIR:PATH=/opt/ros/kinetic/share/actionlib/cmake //The directory
containing a CMake configuration file for actionlib_msgs.
actionlib_msgs_DIR:PATH=/opt/ros/kinetic/share/actionlib_msgs/cmake //The
directory containing a CMake configuration file for angles.
angles_DIR:PATH=/opt/ros/kinetic/share/angles/cmake //The directory containing
a CMake configuration file for bond.
bond_DIR:PATH=/opt/ros/kinetic/share/bond/cmake //The directory containing a
CMake configuration file for bondcpp.
bondcpp_DIR:PATH=/opt/ros/kinetic/share/bondcpp/cmake //Value Computed by
CMake
camera_calibration_parsers_BINARY_DIR:STATIC=/home/robuter/catkin_ws/build/image_common/camera_calibration_parsers
//The directory containing a CMake configuration file for
camera_calibration_parsers.
camera_calibration_parsers_DIR:PATH=/home/robuter/catkin_ws/devel/share/camera_calibration_parsers/cmake
//Dependencies for the target
camera_calibration_parsers_LIB_DEPENDS:STATIC=general;/opt/ros/kinetic/lib/libroscpp.so;general;/usr/lib/x86_64-linux-
gnu/libboost_signals.so;general;/usr/lib/x86_64-linux-
gnu/libboost_filesystem.so;general;/opt/ros/kinetic/lib/librosconsole.so;general;/opt/ros/kinetic/lib/librosconsole_log4cxx.so;general;/opt/ros/kinetic/lib/librosconsole_backend_interface.so;general;/usr/lib/x86_64-linux-
gnu/liblog4cxx.so;general;/usr/lib/x86_64-linux-
gnu/libboost_regex.so;general;/opt/ros/kinetic/lib/libxmlrpcpp.so;general;/opt/ros/kinetic/lib/libroscpp_serialization.so;general;/opt/ros/kinetic/lib/librostime.so;general;/opt/ros/kinetic/lib/libcpp_common.so;general;/usr/lib/x86_64-linux-


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Delete


CMakeFiles/moveit_move_group_default_capabilities.dir/src/default_capabilities/cartesian_path_service_capability.cpp.o
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[100%] Built target moveit_move_group_default_capabilities
<== Finished processing package [15 of 26]: 'moveit_ros_move_group'

==> Processing catkin package: 'moveit_ros_manipulation'
==> Building with env:
'/tmp/catkin_workspace/devel_isolated/moveit_ros_move_group/env.sh'
==> cmake /tmp/catkin_workspace/src/moveit/moveit_ros/manipulation
-DCATKIN_DEVEL_PREFIX=/tmp/catkin_workspace/devel_isolated/moveit_ros_manipulation
-DCMAKE_INSTALL_PREFIX=/tmp/catkin_workspace/install_isolated
-DCATKIN_ENABLE_TESTING=1 -DCATKIN_SKIP_TESTING=0
-DCATKIN_TEST_RESULTS_DIR=/tmp/catkin_workspace/test_results -G Unix Makefiles
in '/tmp/catkin_workspace/build_isolated/moveit_ros_manipulation'
\-- Using CATKIN_DEVEL_PREFIX:
/tmp/catkin_workspace/devel_isolated/moveit_ros_manipulation
\-- Using CMAKE_PREFIX_PATH:
/tmp/catkin_workspace/devel_isolated/moveit_ros_move_group;/tmp/catkin_workspace/devel_isolated/moveit_planners_ompl;/tmp/catkin_workspace/devel_isolated/moveit_kinematics;/tmp/catkin_workspace/devel_isolated/moveit_fake_controller_manager;/tmp/catkin_workspace/devel_isolated/moveit_ros_planning;/tmp/catkin_workspace/devel_isolated/moveit_ros_perception;/tmp/catkin_workspace/devel_isolated/moveit_ros;/tmp/catkin_workspace/devel_isolated/moveit_plugins;/tmp/catkin_workspace/devel_isolated/moveit_planners;/tmp/catkin_workspace/devel_isolated/chomp_motion_planner;/tmp/catkin_workspace/devel_isolated/moveit_experimental;/tmp/catkin_workspace/devel_isolated/moveit_controller_manager_example;/tmp/catkin_workspace/devel_isolated/moveit_core;/tmp/catkin_workspace/devel_isolated/moveit_commander;/tmp/catkin_workspace/devel_isolated/moveit;/opt/ros/lunar
\-- This workspace overlays:
/tmp/catkin_workspace/devel_isolated/moveit_ros_move_group;/tmp/catkin_workspace/devel_isolated/moveit_planners_ompl;/tmp/catkin_workspace/devel_isolated/moveit_kinematics;/tmp/catkin_workspace/devel_isolated/moveit_fake_controller_manager;/tmp/catkin_workspace/devel_isolated/moveit_ros_planning;/tmp/catkin_workspace/devel_isolated/moveit_ros_perception;/tmp/catkin_workspace/devel_isolated/moveit_ros;/tmp/catkin_workspace/devel_isolated/moveit_plugins;/tmp/catkin_workspace/devel_isolated/moveit_planners;/tmp/catkin_workspace/devel_isolated/chomp_motion_planner;/tmp/catkin_workspace/devel_isolated/moveit_experimental;/tmp/catkin_workspace/devel_isolated/moveit_controller_manager_example;/tmp/catkin_workspace/devel_isolated/moveit_core;/tmp/catkin_workspace/devel_isolated/moveit_commander;/tmp/catkin_workspace/devel_isolated/moveit;/opt/ros/lunar


roslaunch (MoveIt-node-folder-name) demo.launch


Aug 23, 2018, 12:29:37 AM  8/23/18


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###  ROS Buildfarm


\-- Build files have been written to:
/tmp/catkin_workspace/build_isolated/moveit_ros_control_interface
==> make -j1 in
'/tmp/catkin_workspace/build_isolated/moveit_ros_control_interface'
Scanning dependencies of target moveit_ros_control_interface_trajectory_plugin
[ 25%] Building CXX object
CMakeFiles/moveit_ros_control_interface_trajectory_plugin.dir/src/joint_trajectory_controller_plugin.cpp.o
[ 50%] Linking CXX shared library
/tmp/catkin_workspace/devel_isolated/moveit_ros_control_interface/lib/libmoveit_ros_control_interface_trajectory_plugin.so
[ 50%] Built target moveit_ros_control_interface_trajectory_plugin
Scanning dependencies of target moveit_ros_control_interface_plugin
[ 75%] Building CXX object
CMakeFiles/moveit_ros_control_interface_plugin.dir/src/controller_manager_plugin.cpp.o
[100%] Linking CXX shared library
/tmp/catkin_workspace/devel_isolated/moveit_ros_control_interface/lib/libmoveit_ros_control_interface_plugin.so
[100%] Built target moveit_ros_control_interface_plugin
<== Finished processing package [26 of 26]: 'moveit_ros_control_interface'
# END SUBSECTION
# BEGIN SUBSECTION: build tests
Invoking '. /opt/ros/lunar/setup.sh && PYTHONIOENCODING=utf_8
PYTHONUNBUFFERED=1 catkin_make_isolated --cmake-args -DCATKIN_ENABLE_TESTING=1
-DCATKIN_SKIP_TESTING=0
-DCATKIN_TEST_RESULTS_DIR=/tmp/catkin_workspace/test_results --catkin-make-
args -j1 tests' in '/tmp/catkin_workspace'
Base path: /tmp/catkin_workspace
Source space: /tmp/catkin_workspace/src
Build space: /tmp/catkin_workspace/build_isolated
Devel space: /tmp/catkin_workspace/devel_isolated
Install space: /tmp/catkin_workspace/install_isolated
Additional CMake Arguments: -DCATKIN_ENABLE_TESTING=1 -DCATKIN_SKIP_TESTING=0
-DCATKIN_TEST_RESULTS_DIR=/tmp/catkin_workspace/test_results
Additional make Arguments for catkin packages: -j1 tests
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~ traversing 26 packages in topological order:
~~ - moveit
~~ - moveit_commander
~~ - moveit_core
~~ - moveit_controller_manager_example
~~ - moveit_experimental
~~ - chomp_motion_planner


to mikael+b...@osrfoundation.org, ros-build...@googlegroups.com,
13...@2000.jukuin.keio.ac.jp, ch...@live.in, da...@dav.ee,
g.a.van...@tudelft.nl, gm1...@gmail.com, imcma...@gmail.com,
jon.b...@gmail.com, jorge...@swri.org, mathias...@ipa.fraunhofer.de,
m...@v4hn.de, mfer...@fetchrobotics.com, moveit_r...@googlegroups.com,
rhas...@techfak.uni-bielefeld.de


[ HEBI Motor Control Strategies
](https://docs.hebi.us/core_concepts.html#control-strategies)


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unread,


##  How to run a MoveIt pipeline


Main menu


[ ![Logo](../../../_static/humble-small.png) ](../../../index.html)
  * [ Tutorials ](../../tutorials/tutorials.html)
  * [ Examples ](../examples.html)
    * [ MoveGroup - ROS Wrappers in C++ ](../examples.html#movegroup-ros-wrappers-in-c)
    * [ Using MoveIt Directly Through the C++ API ](../examples.html#using-moveit-directly-through-the-c-api)
    * [ Integration with a New Robot ](../examples.html#integration-with-a-new-robot)
      * MoveIt Setup Assistant
        * Overview
        * Getting Started
        * Step 1: Start
        * Step 2: Generate Self-Collision Matrix
        * Step 3: Add Virtual Joints
        * Step 4: Add Planning Groups
        * Step 5: Add Robot Poses
        * Step 6: Label End Effectors
        * Step 7: Add Passive Joints
        * Step 8: 3D Perception
        * Step 9: Gazebo Simulation
        * Step 10: ROS Control
        * Step 11: Add Author Information
        * Step 12: Generate Configuration Files
        * What’s Next
        * Additional Reading
      * [ URDF and SRDF ](../urdf_srdf/urdf_srdf_tutorial.html)
      * [ Low Level Controllers ](../controller_configuration/controller_configuration_tutorial.html)
      * [ Perception Pipeline Tutorial ](../perception_pipeline/perception_pipeline_tutorial.html)
      * [ Hand-Eye Calibration ](../hand_eye_calibration/hand_eye_calibration_tutorial.html)
      * [ IKFast Kinematics Solver ](../ikfast/ikfast_tutorial.html)
      * [ TRAC-IK Kinematics Solver ](../trac_ik/trac_ik_tutorial.html)
    * [ Configuration ](../examples.html#configuration)
    * [ Miscellaneous ](../examples.html#miscellaneous)
  * [ Concepts ](../../concepts/concepts.html)
  * [ How-To Guides ](../../how_to_guides/how_to_guides.html)
  * [ API Documentation ](../../api/api.html)
  * [ Contributing ](../../how_to_contribute/how_to_contribute.html)
__ [ MoveIt Documentation: Humble ](../../../index.html)
  * [ ](../../../index.html)
  * [ Examples ](../examples.html)
  * MoveIt Setup Assistant
  * [ Edit on GitHub ](https://github.com/ros-planning/moveit2_tutorials/blob/humble/doc/examples/setup_assistant/setup_assistant_tutorial.rst)


NOTE: The HEBI’s hebi_description repository contains .xml marco files for
their motors and brackets. So if you are using HEBI as part of your robot, it
would be best to incorporate these xacro macros in your robot model xacro and
URDF models.


LIBFCL_STATIC_LIBS:INTERNAL= LIBFCL_STATIC_LIBS_L:INTERNAL=
LIBFCL_STATIC_LIBS_OTHER:INTERNAL= LIBFCL_STATIC_LIBS_PATHS:INTERNAL=
LIBFCL_VERSION:INTERNAL=0.5.0 LIBFCL_fcl_INCLUDEDIR:INTERNAL=
LIBFCL_fcl_LIBDIR:INTERNAL= LIBFCL_fcl_PREFIX:INTERNAL=
LIBFCL_fcl_VERSION:INTERNAL= NEW_YAMLCPP_CFLAGS:INTERNAL=
NEW_YAMLCPP_CFLAGS_I:INTERNAL= NEW_YAMLCPP_CFLAGS_OTHER:INTERNAL=
NEW_YAMLCPP_FOUND:INTERNAL=1 NEW_YAMLCPP_INCLUDEDIR:INTERNAL=/usr/include
NEW_YAMLCPP_INCLUDE_DIRS:INTERNAL= NEW_YAMLCPP_LDFLAGS:INTERNAL=-lyaml-cpp
NEW_YAMLCPP_LDFLAGS_OTHER:INTERNAL= NEW_YAMLCPP_LIBDIR:INTERNAL=
NEW_YAMLCPP_LIBRARIES:INTERNAL=yaml-cpp NEW_YAMLCPP_LIBRARY_DIRS:INTERNAL=
NEW_YAMLCPP_LIBS:INTERNAL= NEW_YAMLCPP_LIBS_L:INTERNAL=
NEW_YAMLCPP_LIBS_OTHER:INTERNAL= NEW_YAMLCPP_LIBS_PATHS:INTERNAL=
NEW_YAMLCPP_PREFIX:INTERNAL=/usr NEW_YAMLCPP_STATIC_CFLAGS:INTERNAL=
NEW_YAMLCPP_STATIC_CFLAGS_I:INTERNAL=
NEW_YAMLCPP_STATIC_CFLAGS_OTHER:INTERNAL=
NEW_YAMLCPP_STATIC_INCLUDE_DIRS:INTERNAL=
NEW_YAMLCPP_STATIC_LDFLAGS:INTERNAL=-lyaml-cpp
NEW_YAMLCPP_STATIC_LDFLAGS_OTHER:INTERNAL= NEW_YAMLCPP_STATIC_LIBDIR:INTERNAL=
NEW_YAMLCPP_STATIC_LIBRARIES:INTERNAL=yaml-cpp
NEW_YAMLCPP_STATIC_LIBRARY_DIRS:INTERNAL= NEW_YAMLCPP_STATIC_LIBS:INTERNAL=
NEW_YAMLCPP_STATIC_LIBS_L:INTERNAL= NEW_YAMLCPP_STATIC_LIBS_OTHER:INTERNAL=
NEW_YAMLCPP_STATIC_LIBS_PATHS:INTERNAL= NEW_YAMLCPP_VERSION:INTERNAL=0.5.2
NEW_YAMLCPP_yaml-cpp_INCLUDEDIR:INTERNAL= NEW_YAMLCPP_yaml-
cpp_LIBDIR:INTERNAL= NEW_YAMLCPP_yaml-cpp_PREFIX:INTERNAL= NEW_YAMLCPP_yaml-
cpp_VERSION:INTERNAL= OGRE_CFLAGS:INTERNAL=-pthread;-I/usr/include/OGRE
OGRE_CFLAGS_I:INTERNAL= OGRE_CFLAGS_OTHER:INTERNAL=-pthread
OGRE_FOUND:INTERNAL=1 OGRE_INCLUDEDIR:INTERNAL=/usr/include
OGRE_INCLUDE_DIRS:INTERNAL=/usr/include/OGRE
OGRE_LDFLAGS:INTERNAL=-lOgreMain;-lpthread OGRE_LDFLAGS_OTHER:INTERNAL=
OGRE_LIBDIR:INTERNAL=/usr/lib/x86_64-linux-gnu
OGRE_LIBRARIES:INTERNAL=OgreMain;pthread OGRE_LIBRARY_DIRS:INTERNAL=
OGRE_LIBS:INTERNAL= OGRE_LIBS_L:INTERNAL= OGRE_LIBS_OTHER:INTERNAL=
OGRE_LIBS_PATHS:INTERNAL= OGRE_OGRE_INCLUDEDIR:INTERNAL=
OGRE_OGRE_LIBDIR:INTERNAL= OGRE_OGRE_PREFIX:INTERNAL=
OGRE_OGRE_VERSION:INTERNAL= OGRE_PREFIX:INTERNAL=/usr
OGRE_STATIC_CFLAGS:INTERNAL=-pthread;-I/usr/include/OGRE
OGRE_STATIC_CFLAGS_I:INTERNAL= OGRE_STATIC_CFLAGS_OTHER:INTERNAL=-pthread
OGRE_STATIC_INCLUDE_DIRS:INTERNAL=/usr/include/OGRE
OGRE_STATIC_LDFLAGS:INTERNAL=-lOgreMain;-lpthread
OGRE_STATIC_LDFLAGS_OTHER:INTERNAL= OGRE_STATIC_LIBDIR:INTERNAL=


![ROS Buildfarm's profile photo](https://lh3.googleusercontent.com/a/default-
user=s40-c)


\-- Build files have been written to:
/tmp/catkin_workspace/build_isolated/moveit_ros_manipulation
==> make -j1 in '/tmp/catkin_workspace/build_isolated/moveit_ros_manipulation'
[ 8%] Generating dynamic reconfigure files from
pick_place/cfg/PickPlaceDynamicReconfigure.cfg:
/tmp/catkin_workspace/devel_isolated/moveit_ros_manipulation/include/moveit_ros_manipulation/PickPlaceDynamicReconfigureConfig.h
/tmp/catkin_workspace/devel_isolated/moveit_ros_manipulation/lib/python2.7/dist-
packages/moveit_ros_manipulation/cfg/PickPlaceDynamicReconfigureConfig.py
Generating reconfiguration files for PickPlaceDynamicReconfigure in
moveit_ros_manipulation
Wrote header file in
/tmp/catkin_workspace/devel_isolated/moveit_ros_manipulation/include/moveit_ros_manipulation/PickPlaceDynamicReconfigureConfig.h
[ 8%] Built target moveit_ros_manipulation_gencfg
Scanning dependencies of target moveit_pick_place_planner
[ 16%] Building CXX object
pick_place/CMakeFiles/moveit_pick_place_planner.dir/src/pick_place_params.cpp.o
[ 25%] Building CXX object
pick_place/CMakeFiles/moveit_pick_place_planner.dir/src/manipulation_pipeline.cpp.o


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\-- Configuring done
\-- Generating done


Show original message


\-- Build files have been written to:
/tmp/catkin_workspace/build_isolated/moveit_planners_ompl
==> make -j1 in '/tmp/catkin_workspace/build_isolated/moveit_planners_ompl'
[ 4%] Generating dynamic reconfigure files from
ompl_interface/cfg/OMPLDynamicReconfigure.cfg:
/tmp/catkin_workspace/devel_isolated/moveit_planners_ompl/include/moveit_planners_ompl/OMPLDynamicReconfigureConfig.h
/tmp/catkin_workspace/devel_isolated/moveit_planners_ompl/lib/python2.7/dist-
packages/moveit_planners_ompl/cfg/OMPLDynamicReconfigureConfig.py
Generating reconfiguration files for OMPLDynamicReconfigure in
moveit_planners_ompl
Wrote header file in
/tmp/catkin_workspace/devel_isolated/moveit_planners_ompl/include/moveit_planners_ompl/OMPLDynamicReconfigureConfig.h
[ 4%] Built target moveit_planners_ompl_gencfg
Scanning dependencies of target moveit_ompl_interface
[ 8%] Building CXX object
ompl_interface/CMakeFiles/moveit_ompl_interface.dir/src/ompl_interface.cpp.o
[ 12%] Building CXX object
ompl_interface/CMakeFiles/moveit_ompl_interface.dir/src/planning_context_manager.cpp.o
[ 16%] Building CXX object
ompl_interface/CMakeFiles/moveit_ompl_interface.dir/src/constraints_library.cpp.o


  * [ Wiki ](/wiki/)
  * [ About ](/about/)
  * [ Contribute ](/docs/)


[moveit_ros.add_time_optimal_parameterization]: Param 'move_group.resample_dt'
was not set. Using default value: 0.100000
|  [move_group-4] [INFO] [1630341477.796659674]
[moveit_ros.add_time_optimal_parameterization]: Param
'move_group.min_angle_change' was not set. Using default value: 0.001000
|  [move_group-4] [INFO] [1630341477.796676547]
[moveit_ros.fix_workspace_bounds]: Param 'move_group.default_workspace_bounds'
was not set. Using default value: 10.000000
|  [move_group-4] [INFO] [1630341477.796692908]
[moveit_ros.fix_start_state_bounds]: Param
'move_group.start_state_max_bounds_error' was set to 0.050000
|  [move_group-4] [INFO] [1630341477.796697822]
[moveit_ros.fix_start_state_bounds]: Param 'move_group.start_state_max_dt' was
not set. Using default value: 0.500000
|  [move_group-4] [INFO] [1630341477.796709089]
[moveit_ros.fix_start_state_collision]: Param 'move_group.start_state_max_dt'
was not set. Using default value: 0.500000
|  [move_group-4] [INFO] [1630341477.796714033]
[moveit_ros.fix_start_state_collision]: Param 'move_group.jiggle_fraction' was
not set. Using default value: 0.020000
|  [move_group-4] [INFO] [1630341477.796728966]
[moveit_ros.fix_start_state_collision]: Param
'move_group.max_sampling_attempts' was not set. Using default value: 100
|  [move_group-4] [INFO] [1630341477.796740400]
[moveit.ros_planning.planning_pipeline]: Using planning request adapter 'Add
Time Optimal Parameterization'
|  [move_group-4] [INFO] [1630341477.796746532]
[moveit.ros_planning.planning_pipeline]: Using planning request adapter 'Fix
Workspace Bounds'
|  [move_group-4] [INFO] [1630341477.796749920]
[moveit.ros_planning.planning_pipeline]: Using planning request adapter 'Fix
Start State Bounds'
|  [move_group-4] [INFO] [1630341477.796752995]
[moveit.ros_planning.planning_pipeline]: Using planning request adapter 'Fix
Start State In Collision'
|  [move_group-4] [INFO] [1630341477.796784074]
[moveit.ros_planning.planning_pipeline]: Using planning request adapter 'Fix
Start State Path Constraints'
|  [mongo_wrapper_ros.py-6] [INFO] [1630341477.864291895] [mongodb]: Starting
mongodb with db location /tmp/db listening on localhost:33829


PCL_SEARCH_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-gnu/libpcl_search.so //path
to pcl_search library debug
PCL_SEARCH_LIBRARY_DEBUG:FILEPATH=/usr/lib/x86_64-linux-gnu/libpcl_search.so
//path to segmentation headers
PCL_SEGMENTATION_INCLUDE_DIR:PATH=/usr/include/pcl-1.7 //path to
pcl_segmentation library
PCL_SEGMENTATION_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-
gnu/libpcl_segmentation.so //path to pcl_segmentation library debug
PCL_SEGMENTATION_LIBRARY_DEBUG:FILEPATH=/usr/lib/x86_64-linux-
gnu/libpcl_segmentation.so //path to surface headers
PCL_SURFACE_INCLUDE_DIR:PATH=/usr/include/pcl-1.7 //path to pcl_surface
library PCL_SURFACE_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-
gnu/libpcl_surface.so //path to pcl_surface library debug
PCL_SURFACE_LIBRARY_DEBUG:FILEPATH=/usr/lib/x86_64-linux-gnu/libpcl_surface.so
//path to tracking headers PCL_TRACKING_INCLUDE_DIR:PATH=/usr/include/pcl-1.7
//path to pcl_tracking library
PCL_TRACKING_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-gnu/libpcl_tracking.so
//path to pcl_tracking library debug
PCL_TRACKING_LIBRARY_DEBUG:FILEPATH=/usr/lib/x86_64-linux-
gnu/libpcl_tracking.so //path to visualization headers
PCL_VISUALIZATION_INCLUDE_DIR:PATH=/usr/include/pcl-1.7 //path to
pcl_visualization library
PCL_VISUALIZATION_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-
gnu/libpcl_visualization.so //path to pcl_visualization library debug
PCL_VISUALIZATION_LIBRARY_DEBUG:FILEPATH=/usr/lib/x86_64-linux-
gnu/libpcl_visualization.so //pkg-config executable
PKG_CONFIG_EXECUTABLE:FILEPATH=/usr/bin/pkg-config //Path to a program.
PYTHON_EXECUTABLE:FILEPATH=/usr/bin/python //Path to a file.
PYTHON_INCLUDE_DIR:PATH=/usr/include/python2.7 //Path to a library.
PYTHON_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-gnu/libpython2.7.so //Path to a
library. PYTHON_LIBRARY_DEBUG:FILEPATH=PYTHON_LIBRARY_DEBUG-NOTFOUND //Specify
specific Python version to use ('major.minor' or 'major')
PYTHON_VERSION:STRING= //Value Computed by CMake
Project_BINARY_DIR:STATIC=/home/robuter/catkin_ws/build //Value Computed by
CMake Project_SOURCE_DIR:STATIC=/home/robuter/catkin_ws/src //Path to a file.
QHULL_INCLUDE_DIRS:PATH=/usr/include //Path to a library.
QHULL_LIBRARY:FILEPATH=/usr/lib/x86_64-linux-gnu/libqhull.so //Path to a
library. QHULL_LIBRARY_DEBUG:FILEPATH=/usr/lib/x86_64-linux-gnu/libqhull.so
//The directory containing a CMake configuration file for Qt5Core.
Qt5Core_DIR:PATH=/usr/lib/x86_64-linux-gnu/cmake/Qt5Core //The directory


Outsight


Yet, no matter how advanced and sophisticated sensor technologies become, they
are nothing more than computers; and connected computers are always at risk of
cyberattacks. Therefore, just as we trust these sensors to take over our
wheels, cybersecurity measures must be in place to ensure they do not get
tampered with by malicious actors. The automotive cybersecurity regulation
outlined by [ _WP.29_ ](https://autocrypt.io/services/wp-29/) ensures that all
OEMs build their vehicles with secure cybersecurity systems in place, so that
we can all trust the sensors to do their job.


12 April, 2024


Radar (radio detection and ranging) was first invented prior to World War II
and has been widely used since then to precisely track the position, speed,
and direction of aircraft and ships. It was first brought into cars by
Mercedes-Benz in 1999 to support its adaptive speed feature. Radar technology
can be broken down into a transmitter and a receiver. The transmitter blasts
radio waves in a targeted direction. These radio waves then get reflected when
they reach any significant object. The receiver picks up these reflected waves
and analyzes them to identify the location, speed, and direction of the
object.


1) the inherently **different types of perception** they provide, and 2) the
potential **privacy concerns** they may give rise to or help evade.


](https://www.linkedin.com/pulse/getting-started-keras-madhavan-
vivekanandan-68dwe?trk=article-ssr-frontend-pulse_more-articles_related-
content-card)


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Radar and LiDAR are **Active sensors** , different from Cameras, that are
**Passive** .


##  Active vs. Passive Sensors


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* **Weather conditions can significantly affect passive sensors** since their sensing doesn't directly interact with physical phenomena like rain or fog. Instead, they can only work with the resulting image, making them more susceptible to weather-related limitations. However, active sensors can also be affected by adverse conditions, depending on their wavelength.


Love


corresponding row pixel unit, and the image signals in the row pixel unit are transmitted to the
corresponding analog signal processing unit and digital-to-analog conversion circuit through the signal
bus of their respective columns, which are converted into digital image signals and output [6]. Cameras
are usually divided into the monocular cameras and binocular camera. In general, the perspective of the
monocular vehicle camera is from 50° to 60°, and the visual distance is from 100m to 200m. The
binocular camera can simulate human visual imaging for 3D imaging, compare the different image
signals obtained by two cameras, identify the object more reliably, and obtain the distance and speed
information of the object through the algorithm.
Compare with LiDAR, the cameras can use natural light during the day. It can recognize the color of
cars and traffic lights and recognize objects far away with higher resolution and lower costs in full-light
conditions. However, it also has some disadvantages. For example, it is easy to be affected by rain and
snow weather and illumination, the change of illumination has a great impact on its recognition accuracy,
and the current camera technology is difficult to identify distant objects in static images.
MAEIE 2021
Journal of Physics: Conference Series 2093 (2021) 012032
IOP Publishing
doi:10.1088/1742-6596/2093/1/012032
4
2.3. Comparison
Figure 3 Comparison of sensing capabilities of human drivers and sensors in highly automated
vehicles
As can be seen from Figure 3, different sensors have different principles and functions, which can play
their advantages in different use scenarios and are difficult to replace each other.[7] For example, if
LiDAR is mounted on the roof of the car, then the underside of the vehicle is a circular area that cannot
be measured. In the future, it is impossible to realize intelligent driving and autonomous driving only by
relying on a single sensor. An important trend is to combine different sensors so that they can
complement each other and ultimately improve accuracy. Today's driverless vehicles often get
information by combining data from multiple sensors, such as LiDAR, cameras, and millimeter-wave
radar.
3. Comparison between computer vision and LiDAR
There are currently two schools of thought for perceptual solutions in autonomous driving technology:
computer vision (camera) school and LiDAR school. The computer vision school thinks LiDAR is
unnecessary and too expensive; LiDAR advocates believe that the computer vision algorithm is
insufficient in data form and precision. The following will analyze the two schools respectively.
3.1. Computer Vision
Humans can use their brains to estimate distances. They also have some gifts, such as stereoscopic vision,
but only for estimating medium distances. Another gift is motion parallax. The movement direction and
speed of objects seen when the line of sight moves laterally in the field of view are different, which also
provides information for judging the distance. So, the human brain is perfectly up to the task, and in fact,
it is possible to estimate distance while driving with one eye open. Now, researchers are trying to use
neural networks to build machine learning techniques to estimate the distance from an image.
A technical paper from Cornell University proposes a new approach to closing the performance gap
between pure computer vision technology architectures and LiDAR [8]. By changing the 3D information
presentation form of the target detection system of the stereo camera, this paper converts the imagebased stereo vision data into 3D point clouds similar to those generated by LiDAR, and then converts
the data into the final view format. Using a relatively inexpensive camera on either side of the windshield,
the new method has brought the performance of the camera close to that of LiDAR for object detection.
On the KITTI standard dataset, the detection accuracy of the proposed method is improved from 22%
to 74% in the range of 30m.
The study suggests that it is possible to use stereoscopic cameras in autonomous vehicles, which
could significantly reduce costs and improve safety. But it will be a long time before cameras replace
LiDAR.
MAEIE 2021
Journal of Physics: Conference Series 2093 (2021) 012032
IOP Publishing
doi:10.1088/1742-6596/2093/1/012032
5
3.2. LiDAR
Compared with the camera, LiDAR has a powerful three-dimensional spatial resolution ability. At
present, in the field of autonomous driving and assisted driving, LiDAR is one of the core sensors to
realize environmental perception. Among the sensors used for road information detection, LiDAR has


Published Jul 6, 2023


**Camera** is a type of sensor that uses optical images to identify objects.
It is a relatively inexpensive sensor that can see in low-light conditions,
but it has a limited range and cannot see objects that are obscured by other
objects.


Beyond night vision, the impact of external lighting on cameras has far-
reaching consequences. For instance, Computer Vision algorithms may fail in
areas with shadows caused by objects (moving or static _e.g. trees or
buildings_ ) and even in indoor settings when lighting conditions change
_(e.g., a door opening adding more light to the scene)._


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###  Keras Layers: The Essential Building Blocks for Deep Learning


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_ [2]  Governor’s Highway Safety Association, Pedestrian Traffic Fatalities by
State: 2020 Preliminary Data 16 (2021). _


###  **Are Sensors Reliable?**


    [Mat](class_cv_Mat.html#doxid-db-de6-classcv-1-1-mat) statePre

predicted state (x’(k)): x(k)=A*x(k-1)+B*u(k)

    [Mat](class_cv_Mat.html#doxid-db-de6-classcv-1-1-mat) transitionMatrix

state transition matrix (A)
###  Construction

    KalmanFilter()

The constructors.
In C API when CvKalman* kalmanFilter structure is not needed anymore, it
should be released with cvReleaseKalman(&kalmanFilter)

    KalmanFilter(
        int dynamParams,
        int measureParams,
        int controlParams = 0,
        int type = [CV_32F](group_core_hal_interface.html#doxid-d1-d1b-group-core-hal-interface-1ga4a3def5d72b74bed31f5f8ab7676099c)
        )

This is an overloaded member function, provided for convenience. It differs
from the above function only in what argument(s) it accepts.
Parameters:
dynamParams  |  Dimensionality of the state.
---|---
measureParams  |  Dimensionality of the measurement.
controlParams  |  Dimensionality of the control vector.
type  |  Type of the created matrices that should be CV_32F or CV_64F.

###  Methods

    const [Mat](class_cv_Mat.html#doxid-db-de6-classcv-1-1-mat)&
    correct(const [Mat](class_cv_Mat.html#doxid-db-de6-classcv-1-1-mat)& measurement)

Updates the predicted state from the measurement.
Parameters:
measurement  |  The measured system parameters
---|---

    void
    init(
        int dynamParams,
        int measureParams,
        int controlParams = 0,
        int type = [CV_32F](group_core_hal_interface.html#doxid-d1-d1b-group-core-hal-interface-1ga4a3def5d72b74bed31f5f8ab7676099c)
        )

Re-initializes Kalman filter. The previous content is destroyed.
Parameters:
dynamParams  |  Dimensionality of the state.
---|---
measureParams  |  Dimensionality of the measurement.
controlParams  |  Dimensionality of the control vector.


Full Name*


![Graphical image of Lidar, camera, and Radar](https://www-
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![Spider chart showing the capabilities of radar vs. lidar vs.
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**Advantages of LiDAR:**


Presently, Radar primarily detects information in 2D, making it suitable for
various Automotive use cases but less applicable in many other contexts.


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One such tool is the first Multi-Vendor LiDAR Simulator, an online platform
that empowers our partners and customers to make informed decisions about
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In ADAS, radar, lidar, and cameras are often used together to provide a more
complete view of the environment around the vehicle. This allows the ADAS
system to make better decisions about how to respond to hazards.


##  Other aspects to consider


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Autonomous driving is enabled by two sets of technologies: V2X and ADAS. V2X
(vehicle-to-everything) utilizes wireless communication technology to
facilitate real-time interactions between the vehicle and its surrounding
objects and infrastructure. On the other hand, ADAS (advanced driver-
assistance systems) make use of built-in sensors to detect and calculate the
surrounding environment. Both technologies complement each other to ensure a
safe and seamless autonomous driving experience. We have so far explained how
V2X technology works and the different wireless communication standards
involved, see: [ _DSRC vs. C-V2X: A Detailed Comparison of the 2 Types of V2X
Technologies_ ](https://autocrypt.io/dsrc-vs-c-v2x-a-detailed-comparison-of-
the-2-types-of-v2x-technologies/) . In this article, we will focus on the
technologies behind ADAS and take a deep dive into the three types of commonly
used sensors: camera, radar, and LiDAR.


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**Highly sophisticated.** In order to provide an accurate 3D model of the
environment, LiDAR calculates hundreds of thousands of points every second and
transforms them into actions. This means that LiDAR requires a significant
amount of computing power compared to camera and radar. It also makes LiDAR
prone to system malfunctions and software glitches.


As you can see, each sensor has its own strengths and weaknesses. Radar is
accurate and long-range, but it is not as good at identifying objects as lidar
or cameras. Lidar is accurate and high-resolution, but it is expensive and can
be affected by weather. Cameras are high-resolution and can identify objects,
but they can be affected by glare and have a limited range.


![Image showing cameras lack of depth ](https://www-
assets.outsight.ai/ghost/2023/08/optical-illusions-of-camera.jpg)


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  * **Passive sensors are typically more energy-efficient** , whereas active sensors allocate a significant portion of their energy budget to emit laser light or radar waves. The same principle applies to their size and weight.
  * **Passive sensors like Cameras are highly stealthy and difficult to detect** . On the contrary, active sensors like Lidar or Radar emit signals that can be detected by other sensors, making them more conspicuous. This aspect holds paramount importance in Defense applications.
  * Similarly, **Active sensors can encounter signal interference** , known as cross-talking. Nevertheless, with the ongoing advancement of technology, this issue is progressively becoming less of a concern for LiDAR.


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This remarkable capability has expanded to encompass the identification of
individuals through advanced techniques like FaceID.


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##  Explore topics


![Image of 3D point cloud scan highlighting lidar's innate anonymity
](https://www-assets.outsight.ai/ghost/2023/08/Lidar-does-not-capture-
personal-information.png) Point-cloud data doesn't allow to identify an
individual


**Disadvantages of Radar:**


**Lidar** is a type of sensor that uses laser light to create a 3D map of the
environment. It is a more accurate and precise sensor than radar, but it is
also more expensive and can be affected by weather conditions.


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The following chart presents a summary of the previous comparison points,
along with some additional ones:


###  **Radar**


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Additionally, the Governor’s Highway Safety Association (“GHSA”) of the USA
found in an evaluation of roadway fatalities in 2020, that 75% of pedestrian
fatalities occur at night.  [2]


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**High cost.** As expected, due to the sophistication of the software and the
computing resources needed, the price to implement a set of LiDAR sensors is
the highest among the three.


[ ![Automotive penetration
testing](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAv4AAAG2AQAAAAAv/b+IAAAAAnRSTlMAAHaTzTgAAABASURBVHja7cEBDQAAAMKg909tDjegAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACeDKX2AAEzxC0CAAAAAElFTkSuQmCC)
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This paper uses the method of literary analysis to analyze and compare the advantages and
disadvantages of cameras and LiDAR, and discuss the impact of the fusion of the two sensors in the
field of environmental perception.
2. Basic Principles of Autonomous System
AS shown in Figure 1, the latest revision of SAE J3016, formulated by SAE International, provides a
taxonomy that defines six levels of driving automation in detail, from level 0 to level 5 [2]:
MAEIE 2021
Journal of Physics: Conference Series 2093 (2021) 012032
IOP Publishing
doi:10.1088/1742-6596/2093/1/012032
2
Figure 1 SAE Levels of Driving Automation
However, there are still some gaps in the application of autonomous vehicles to practical and complex
road traffic scenarios. Therefore, the environment perception system is an important part of the
autonomous vehicle. The main task is to identify and classify road obstacles, traffic signs, signal lights,
pedestrians and vehicles, etc., able to analyze and judge the location of the vehicle environment[3]. The
Environment perception system is one of the key technologies of the autonomous vehicle. It is very
important for the accurate understanding of traffic scene semantics and the corresponding behavior
decisions of vehicles, and it is an important guarantee for driving safety and intelligence.
The autonomous system includes the decision layer, perception layer, control layer, as well as the
support of high-precision maps and the Internet of Vehicles. Among them, various hardware sensors in
the perception layer capture the position information of the vehicle and external environment
information, and the existing on-board sensors include LiDAR, camera, millimeter-wave radar, etc.
Based on the input information from the perception layer, the decision layer models the environment,
forms a global understanding, makes decisions and judgments, and sends out the signal instructions for
vehicle execution. Finally, the control layer converts the signal of the decision layer into the action
behaviour of the vehicle [4].
Figure 2 Automatic system architecture
MAEIE 2021
Journal of Physics: Conference Series 2093 (2021) 012032
IOP Publishing
doi:10.1088/1742-6596/2093/1/012032
3
2.1. LiDAR
The LiDAR can sense the surrounding environment information in real time and form high-definition
3D graphics. LiDAR works by emitting pulse modulated light, observing the time difference between
the reflected light and the emitted light to calculate distance, and by scanning or synchronously
measuring multiple beams of light to obtain angle information [5]. It has the advantages of fast response,
long detection distance and high accuracy, so it is the sensor solution with the highest measurement
accuracy among the known environmental measurement solutions.
During the course of the on-board LiDAR system, noise is inevitably included in the point cloud data
obtained by the system due to factors such as acceleration, deceleration, change of driving direction, and
so on. At present, most on-board LiDAR systems can provide the intensity information of the reflected
pulse, which reflects the energy reflected from the target surface and is a reflection of the properties of
different objects. But when the LiDAR systems, weather conditions or other specific conditions are
different, the intensity information reflected by the laser point cloud will also be very different. Moreover,
the on-board LiDAR point cloud data is a kind of blind data. The point cloud data is used as the spatial
coordinate information of the surface of the object, and there is no information about the category to
which the object belongs. This leads to great difficulties in the automatic recognition and feature
extraction of objects such as buildings and human beings in the point cloud data.
LiDAR is too expensive to be used in mass production. There are two potential trends in the market:
one is to reduce the cost of performance by producing LiDAR that can only accomplish a specific task
with a limited detection distance; another is the use of solid-state LiDAR to reduce costs.
2.2. Camera
Cameras are a lower-cost solution for applications where both radar and cameras can be used. The
installation position of the camera in front, side, rear, and built-in. Mainly used for forwarding collision
warning system, lane departure warning system, traffic sign recognition system, parking assistant system,
blind spot monitoring system.
Cameras work by illuminating an array of photosensitive cells with light from the outside, creating
a photoelectric effect that generates a certain electrical charge. The row selection unit selects the


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Journal of Physics: Conference
Series
PAPER • OPEN ACCESS
Research on Comparison of LiDAR and Camera in
Autonomous Driving
To cite this article: Peide Wang 2021 J. Phys.: Conf. Ser. 2093 012032
View the article online for updates and enhancements.
You may also like
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environmental implications
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Hicks
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Autonomous vehicles: classification,
technology and evolution
F B Scurt, T Vesselenyi, R C Tarca et al.
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Content from this work may be used under the terms of theCreative Commons Attribution 3.0 licence. Any further distribution
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Published under licence by IOP Publishing Ltd
MAEIE 2021
Journal of Physics: Conference Series 2093 (2021) 012032
IOP Publishing
doi:10.1088/1742-6596/2093/1/012032
1
Research on Comparison of LiDAR and Camera in
Autonomous Driving
Peide Wang
College of Engineering, Computer Engineering, Auburn University, Main Campus,
Auburn, Alabama, AL 36849 US
*Corresponding author’s e-mail: pzw0020@auburn.edu
Abstract. With the improvement of vehicles automation, autonomous vehicles become one of
the research hotspots. Key technologies of autonomous vehicles mainly include perception,
decision-making, and control. Among them, the environmental perception system, which can
convert the physical world’s information collection into digital signals, is the basis of the
hardware architecture of autonomous vehicles. At present, there are two major schools in the
field of environmental perception: camera which is dominated by computer vision and LiDAR.
This paper analyzes and compares the two majors schools in the field of environmental
perception and concludes that multi-sensor fusion is the solution for future autonomous driving.
1. Introduction
Autonomous driving is an advantageous industry in the field of intelligent travel. Among them, the
perception module plays a vital role in the automatic driving system. The key technologies of
autonomous vehicles mainly include the perception of the road environment, the planning of the driving
path, the intelligent decision-making of vehicle motion behavior and, the realization of adaptive motion
control of the vehicle. At present, the immaturity of the development of environmental perception
technology is still the main reason hindering the improvement of the overall performance of autonomous
vehicles, and it is also the biggest obstacle to the large-scale commercialization of autonomous vehicles.
The sensors currently used in the perception module of autonomous driving mainly include cameras,
millimeter-wave radar, ultrasonic radar, and LiDAR. The camera has the advantages of high resolution,
fast speed, rich information transmission, and low cost; LiDAR has the advantages of accurate 3D
perception and is rich in information [1].
A variety of sensors are used in autonomous vehicles, and different types of sensors complement
each other in function to improve the safety factor of the autonomous driving system. To take advantage
of different sensors, fusion technology plays a key role.


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![Table showing all the aspects that lidars can detect vs.
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The right sensor to use will depend on what aspects do you need to detect


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The use of cameras in facial recognition technology raises legitimate
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###  Semantic Segmentation with Keras


This article explores the capabilities and limitations of each type of sensor,
to provide a clear understanding of why LiDAR has emerged as a strong
contender in computer vision tech race.


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###  EVs and Autonomous Driving: Not a Package Deal


**The Key Differences Lie in Precision: Lidar's Laser-Level Accuracy versus
Radar's Lower Resolution"**


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**Low cost.** Camera is relatively cheaper compared to other types of sensors.
This made it possible for OEMs to introduce better autonomous driving features
to mid-range and even lower-end vehicles.


![Comparison of RADAR LIDAR and Camera in
ADAS](https://media.licdn.com/dms/image/D5612AQEZIcjZ10f1TQ/article-
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**Low-definition modeling.** The radio waves are highly accurate at detecting
objects. Yet, compared to the camera, radar is relatively weak at modeling a
perfectly precise shape of the object. As a result, the system might not be
able to identify exactly what the object is. For instance, unlike the camera,
the radar system normally cannot distinguish bicycles from motorcycles, even
though it has no problem determining their speeds.


Through advanced algorithms, cutting-edge techniques and a full set of tools,
Outsight can derive valuable insights from LiDAR point-cloud data, enabling [
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**Unaffected by weather conditions.** The greatest advantage of radar is that
the transmission of radio waves is not affected by visibility, lighting, and
noise. Therefore, radar performance is consistent across all environmental
conditions.


###  **Camera**


####  Senior Technical Architect at HCL America


Consequently, the significance of safeguarding privacy has gained considerable
attention from policymakers and regulators worldwide. State policies, such as
the General Data Protection Regulation (GDPR) in the European Union, have been
put in place to address these concerns by imposing strict guidelines and
limitations on the usage of cameras and biometric data.


Here are some additional considerations when choosing sensors for ADAS:


In the world of automated processing, cameras have become a ubiquitous choice
not necessarily due to their superiority as sensors for the task, but rather
because of their widespread availability.

As we explored earlier in this article, the use of cameras may be suitable for
certain applications like object classification, where their effectiveness is
evident. However, when it comes to capturing complex Spatial Data of the
physical world, these sensors reveal their limitations.

The intricacies of spatial data processing necessitate more specialised and
sophisticated sensor technologies to overcome challenges and provide more
accurate and comprehensive results.

While Radar shares the benefits of being an active sensor, akin to LiDAR, and
equally excels in preserving privacy, its significantly lower resolution and
precision disqualify it as a viable candidate for numerous use cases, such as
crowd monitoring and precise traffic management.

The orders of magnitude difference in resolution makes Radar less capable of
capturing intricate details and spatial data with the same level of accuracy
as LiDAR.


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This depth perception is not required in many simple Computer Vision tasks,
such as classifying objects, where Cameras excel:


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##  Perceiving Various Dimensions of the World


Related Articles


###  Getting started with Keras


[ ![](https://www-assets.outsight.ai/ghost/2023/09/Outsight-Cover.gif)
Outsight in a nutshell  ](/insights/outsight-in-a-nutshell) [ ![](https://www-
assets.outsight.ai/ghost/2023/11/Why-3D-LiDAR-is-a-Transformative-Technology-
for-Airports.jpg) The Seven reasons why 3D LiDAR is Transforming Airports
](/insights/why-3d-lidar-is-a-transformative-technology-for-airports) [
![](https://www-assets.outsight.ai/ghost/2023/01/Tree.jpg) The Top 101
Applications of LiDAR  ](/insights/the-top-101-lidar-applications) [
![](https://www-assets.outsight.ai/ghost/2023/01/Laser-light-cover.jpg)
Understanding the Basics of 3D LiDAR Technology  ](/insights/understanding-
the-basics-of-3d-lidar-technology) [
![](https://images.unsplash.com/photo-1573509078860-0196070b81dd?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwxMTc3M3wwfDF8c2VhcmNofDg4fHx0ZW4lMjBkd2FyZmVzfGVufDB8fHx8MTY2ODg3MDA5Nw&ixlib=rb-4.0.3&q=80&w=2000)
The 10 Reasons to Use a 3D Software Processor  ](/insights/10-reasons-to-
use-a-3d-software-processor)


####  Mar 31, 2024


[ LiDAR Software Helps Airports to Manage Increased Traffic  See how and why
Airports are increasingly using LiDAR-based software solutions to tackle their
biggest challenges, leveraging the unique value of Spatial Intelligence.
![](https://www2.outsight.ai/favicon.png) LEFIGARO  ![](https://www-
assets.outsight.ai/ghost/2023/02/Airport-Blog-Cover.jpg) ](/insights/lidar-
software-helps-airports-to-manage-increased-traffic)


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**Cameras capture colours in two dimensions** , lacking any notion of depth.
Consequently, a large object positioned far away from the device can have the
same number of pixels as a small object situated close to the camera.


![Image of a rainy day highlighting lidar's capability to function is multiple
weather scenarios
](https://images.unsplash.com/photo-1558409057-bbe679023136?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wxMTc3M3wwfDF8c2VhcmNofDJ8fHJhaW5pbmd8ZW58MHx8fHwxNjkwODg4NTI5fDA&ixlib=rb-4.0.3&q=80&w=2000)


**Disadvantages of Camera:**


![Image of a man holding a picture in front of his
face](https://images.unsplash.com/photo-1529974445367-5b9bf0a0586e?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wxMTc3M3wwfDF8c2VhcmNofDJ8fGZhY2UlMjBpZGVudGlmaWNhdGlvbnxlbnwwfHx8fDE2OTA4OTIzMTV8MA&ixlib=rb-4.0.3&q=80&w=2000)


**Recognizing 2D information.** Since camera is based on imagery, it is the
only sensor with the capability of detecting 2D shapes and colours, making it
crucial to reading lanes and pavement markings. With higher resolutions, even
fading lines and shapes can be read very accurately. Infrared lighting is also
equipped with most modern cameras, making it just as easy to navigate at
night.


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**Advantages of Camera:**


##  Conclusion


However, while LiDAR data, in the form of point-cloud information, provides a
wealth of Spatial data, this raw data alone is essentially useless without
effective processing and analysis.


###  Use Cases for Dense Layers in Keras


####  Apr 9, 2024


Each type of sensor operates in distinct sections of the electromagnetic
spectrum, utilising signals with varying wavelengths.


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