DR 1.3: Sensing, mapping and low level memory III
Loop closure detection on 3D data is a challenging task that has been commonly approached by adapting image based solutions. Methods based on local features suffer from ambiguity and from robustness to environment changes while methods based on global features are viewpoint dependent. We propose SegMatch, a reliable loop closure detection algorithm based on the matching of 3D segments. Segments provide a good compromise between local and global descriptions, incorporating their strengths while reducing their individual drawbacks. SegMatch does not rely on assumptions of 'perfect segmentation' or on the existence of 'objects' in the environment, which allows for reliable execution on large scale, unstructured environments. We quantitatively demonstrate that SegMatch can achieve accurate localization at a frequency of 1Hz on the largest sequence of the KITTI odometry dataset. We furthermore show how this algorithm can reliably detect and close loops in real time, during online operation. In addition, the source code for the SegMatch algorithm is made publicly available. Relation to WP Describes the loop closure strategy for 3D point clouds introduced in WP 1, T1.5, Section 1.3.1. Availablity Unrestricted and available online (https:/arxiv.org/pdf/ 1609.07720v1.pdf) 2.2 Gawel (2016) "Structure based Vision Laser Matching" Bibliography Abel Gawel, Titus Cieslewski, Renaud Dube Mike Bosse, Roland Siegwart and Juan Nieto. "Structure based Vision Laser Matching." In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016. Abstract Persistent merging of maps created by different sensor modalities is an insufficiently addressed problem. Current approaches either rely on appearance based features which may suffer from lighting and viewpoint changes or require pre registration between all sensor modalities used. ThisPersistent merging of maps created by different sensor modalities is an insufficiently addressed problem. Current approaches either rely on appearance based features which may suffer from lighting and viewpoint changes or require pre registration between all sensor modalities used. This EU FP7 TRADR (ICT 60963) 34 DR 1.3: Sensing, mapping and low level memory III T. Svoboda et al. work presents a framework using structural descriptors for matching LIDAR point cloud maps and sparse vision keypoint maps. The matching algorithm works independently of the sensors' viewpoint and varying lighting and does not require pre registration between the sensors used. Furthermore, we employ the approach in a novel vision laser map merging algorithm. We analyse a range of structural descriptors and present results of the method integrated within a full mapping framework. Despite the fact that we match between the visual and laser domains, we can successfully perform map merging using structural descriptors. The effectiveness of the presented structure based vision laser matching is evaluated on the public KITTI dataset and furthermore demonstrated on a map merging problem in an industrial site. Relation to WP Describes a map registration strategy for vision and laser pointclouds introduced in WP 1, T1.5, Section 1.3.1. Availablity Unrestricted and available online (http:/rpg.ifi.uzh.ch/ docs/IROS16_Gawel.pdf) 2.3 Pecka TIE2016, "Controlling Robot Morphology from Incomplete Measurements" Bibliography Pecka, Martin and Zimmermann, Karel and Reinstein, Michal and Svoboda, Tomas. "Controlling Robot Morphology from Incomplete Measurements" In IEEE Transactions on Industrial Electronics, Special issue on: on Motion Control for Novel Emerging Robotic Devices and Systems. Accepted, early access http:/ieeexplore.ieee.org/document/7490379/ Abstract Mobile robots with complex morphology are essential for traversing rough terrains in Urban Search Rescue missions (USAR) Since teleoperation of the complex morphology causes high cognitive load of the operator, the morphology is controlled autonomously. The autonomous control measures the robot state and surrounding terrain which is usually only partially observable, and thus the data are often incomplete. We marginalize the control over the missing measurements and evaluate an explicit safety condition. If the safety condition is violated, tactile terrain exploration by the body mounted robotic arm gathers the missing data.Mobile robots with complex morphology are essential for traversing rough terrains in Urban Search Rescue missions (USAR) Since teleoperation of the complex morphology causes high cognitive load of the operator, the morphology is controlled autonomously. The autonomous control measures the robot state and surrounding terrain which is usually only partially observable, and thus the data are often incomplete. We marginalize the control over the missing measurements and evaluate an explicit safety condition. If the safety condition is violated, tactile terrain exploration by the body mounted robotic arm gathers the missing data. Relation to WP Describes an approach for automatic robot control on rough terrain. Contributes to the robot perception suite. T1.5. Availablity Unrestricted. Included in the public version of this deliverable. https:/arxiv.org/abs/1612.02739. EU FP7 TRADR (ICT 60963) 35 DR 1.3: Sensing, mapping and low level memory III T. Svoboda et al. 2.4 Pecka IROS 2016, "Autonomous Flipper Control with Safety Constraints" Bibliography Martin Pecka, Votjech Salansky, Karel Zimmermann, and Tomas Svoboda. "Autonomous flipper control with safety constraints" In Intelligent Robots and Systems (IROS) 2016 IEEE/RSJ International Conference on Abstract Policy Gradient methods require many real world trials. Some of the trials may endanger the robot system and cause its rapid wear. Therefore, a safe or at least gentle to wear exploration is a desired property. We incorporate bounds on the probability of unwanted trials into the recent Contextual Relative Entropy Policy Search method. The proposed algorithm is evaluated on the task of autonomous flipper control for a real Search and Rescue rover platform.Policy Gradient methods require many real world trials. Some of the trials may endanger the robot system and cause its rapid wear. Therefore, a safe or at least gentle to wear exploration is a desired property. We incorporate bounds on the probability of unwanted trials into the recent Contextual Relative Entropy Policy Search method. The proposed algorithm is evaluated on the task of autonomous flipper control for a real Search and Rescue rover platform. Relation to WP Describes a novel reinforcement learning approch that effectively combines simulator and real roll outs of the ground robot. Contributes to the robot perception suite. T1.5. Availablity Unrestricted. Included in the public version of this deliverable. http:/cmp.felk.cvut.cz/~peckama2/papers/2016_IROS_Autonomous_ Flipper_Control_with_Safety_Constraints.pdf. 2.5 Jasek bc thesis 2016, "Detecting Objects for Autonomous System Verification" Bibliography Jasek, Otakar. Detecting Objects for Autonomous System Verification. Bachelor Thesis, Czech Technical University in Prague. Abstract In this thesis we created a framework for easy evaluation and training of Faster R CNN type of networks. We fine tuned VGG16 and ZFNet networks on our internal Victims dataset as well as standard KITTI dataset. We later showed that VGG16 architecture is far more suitable for fine tuning on data from slightly different training and target domains. This framework can later serve as a baseline for further improvements in the field.In this thesis we created a framework for easy evaluation and training of Faster R CNN type of networks. We fine tuned VGG16 and ZFNet networks on our internal Victims dataset as well as standard KITTI dataset. We later showed that VGG16 architecture is far more suitable for fine tuning on data from slightly different training and target domains. This framework can later serve as a baseline for further improvements in the field. Relation to WP Contributes to the scene analysis. T1.5. Availablity Unrestricted. Available for download. https:/dspace.cvut. cz/bitstream/handle/10467/64667/F3 BP 2016 Jasek Otakar jasek.pdf EU FP7 TRADR (ICT 60963) 36 DR 1.3: Sensing, mapping and low level memory III T. Svoboda et al. 2.6 Kashammer ms thesis 2016, "A semantic interpreter for multimodal and multirobot data" Bibliography Kashammmer, Philipp Florian. "A semantic interpreter for multimodal and multirobot data" Master thesis, Czech Technical University in Prague and Lulea University of Technology. 2016. Abstract Huge natural disaster events can be so devastating that they often overwhelm human rescuers and yet, they seem to occur more often. The TRADR (Long Term HumanRobot Teaming for Robot Assisted Disaster Response) research project aims at developing methodology for heterogeneous teams composed of human rescuers as well as ground and aerial robots. While the robots swarm the disaster sites, equipped with advanced sensors, they collect a huge amount row data that cannot be processed efficiently by humans. Therefore, in the frame of the here presented work, a semantic interpreter has been developed that crawls through the raw data, using state of the art object detection algorithms to identify victim targets and extracts all kinds of information that is relevant for rescuers to plan their missions. Subsequently, this information is restructured by a reasoning process and then stored into a high level database that can be queried accordingly and ensures data constancy.Huge natural disaster events can be so devastating that they often overwhelm human rescuers and yet, they seem to occur more often. The TRADR (Long Term HumanRobot Teaming for Robot Assisted Disaster Response) research project aims at developing methodology for heterogeneous teams composed of human rescuers as well as ground and aerial robots. While the robots swarm the disaster sites, equipped with advanced sen