Lidar Scan Registration Robust to Extreme Motions

TL;DR

This paper introduces a robust point cloud registration method that accounts for motion uncertainties, significantly improving accuracy during extreme velocities and accelerations in robotic localization.

Contribution

The proposed method enhances registration robustness by incorporating motion uncertainties and environment geometry, outperforming existing solutions under extreme motion conditions.

Findings

Reduces translation error by 9.26% in extreme scenarios.

Decreases rotation error by 21.84% in high-acceleration tests.

Compatible with various weighted ICP variants.

Abstract

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.