Continuous Trajectory Estimation for 3D SLAM from Actuated Lidar

We extend the Iterative Closest Point (ICP) algorithm to obtain a method for continuous-time trajectory estimation (CICP) suitable for SLAM from actuated lidar. Traditional solutions to SLAM from actuated lidar rely heavily on the accuracy of an auxiliary pose sensor to form rigid frames. These frames are then used with ICP to obtain accurate pose estimates. However, since lidar records a single range sample at time, any error in inter-sample sensor motion must be accounted for. This is not possible if the frame is treated as a rigid point cloud. In this work, instead of ICP we estimate a continuous-time trajectory that takes into account inter-sample pose errors. The trajectory is represented as a linear combination of basis functions and formulated as a solution to a (sparse) linear system without restrictive assumptions on sensor motion. We evaluate the algorithm on synthetic and real data and show improved accuracy in open-loop SLAM in comparison to state-of-the-art rigid registration methods.