  In order to fuse measurements from multiple sensors mounted on a mobile
robot, it is needed to express them in a common reference system through their
relative spatial transformations. In this paper, we present a method to
estimate the full 6DoF extrinsic calibration parameters of multiple
heterogeneous sensors (Lidars, Depth and RGB cameras) suitable for automatic
execution on a mobile robot. Our method computes the 2D calibration parameters
(x, y, yaw) through a motion-based approach, while for the remaining 3
parameters (z, pitch, roll) it requires the observation of the ground plane for
a short period of time. What set this proposal apart from others is that: i)
all calibration parameters are initialized in closed form, and ii) the scale
ambiguity inherent to motion estimation from a monocular camera is explicitly
handled, enabling the combination of these sensors and metric ones (Lidars,
stereo rigs, etc.) within the same optimization framework. %Additionally,
outlier observations arising from local sensor drift are automatically detected
and removed from the calibration process. We provide a formal definition of the
problem, as well as of the contributed method, for which a C++ implementation
has been made publicly available. The suitability of the method has been
assessed in simulation an with real data from indoor and outdoor scenarios.
Finally, improvements over state-of-the-art motion-based calibration proposals
are shown through experimental evaluation.

Robotics