Real-time SLAM with piecewise-planar surface models and sparse 3D point clouds

This paper reports on the use of planar patches as features in a real-time simultaneous localization and mapping (SLAM) system to model smooth surfaces as piecewise-planar. This approach works well for using observed point clouds to correct odometry error, even when the point cloud is sparse. Such sparse point clouds are easily derived by Doppler velocity log sensors for underwater navigation. Each planar patch contained in this point cloud can be constrained in a factor-graph-based approach to SLAM so that neighboring patches are sufficiently coplanar so as to constrain the robot trajectory, but not so much so that the curvature of the surface is lost in the representation. To validate our approach, we simulated a virtual 6-degree of freedom robot performing a spiral-like survey of a sphere, and provide real-world experimental results for an autonomous underwater vehicle used for automated ship hull inspection. We demonstrate that using the sparse 3D point cloud greatly improves the self-consistency of the map. Furthermore, the use of our piecewise-planar framework provides an additional constraint to multi-session underwater SLAM, improving performance over monocular camera measurements alone.