Lidar Scan Registration Robust to Extreme Motions
This addresses the issue of robust localization for mobile robots in harsh terrains after collisions or extreme maneuvers, though it is incremental as it builds on existing de-skewing and ICP methods.
The paper tackles the problem of lidar scan registration failing under extreme robot motions like high accelerations, and presents a method that reduces translation error by 9.26% and rotation error by 21.84% compared to other solutions in tests with peak accelerations of 200 m/s² and 800 rad/s².
Registration algorithms, such as Iterative Closest Point (ICP), have proven effective in mobile robot localization algorithms over the last decades. However, they are susceptible to failure when a robot sustains extreme velocities and accelerations. For example, this kind of motion can happen after a collision, causing a point cloud to be heavily skewed. While point cloud de-skewing methods have been explored in the past to increase localization and mapping accuracy, these methods still rely on highly accurate odometry systems or ideal navigation conditions. In this paper, we present a method taking into account the remaining motion uncertainties of the trajectory used to de-skew a point cloud along with the environment geometry to increase the robustness of current registration algorithms. We compare our method to three other solutions in a test bench producing 3D maps with peak accelerations of 200 m/s^2 and 800 rad/s^2. In these extreme scenarios, we demonstrate that our method decreases the error by 9.26 % in translation and by 21.84 % in rotation. The proposed method is generic enough to be integrated to many variants of weighted ICP without adaptation and supports localization robustness in harsher terrains.