Ensemble Kalman Filter Based LiDAR Odometry for Skewed Point Clouds Using Scan Slicing.

In the presence of fast motion, point clouds obtained from mechanical spinning LiDAR can be easily distorted due to the slow scanning speed of the LiDAR. Existing LiDAR-only odometry algorithms generally ignore this distortion or compensate by linearly interpolating the estimated relative motion between scans. However, when there are abrupt and nonlinear motion changes, the linear interpolation method poorly compensates for the distortions, which can cause significant drift in motion estimates. In this work, we present a LiDAR-only odometry algorithm that estimates motion by slicing LiDAR scans into shorter times to compensate more agilely for point cloud distortions. Observations from only one small scan slice inevitably lack spatial uniqueness, so the multimodal problem needs to be addressed. For LiDAR-only odometry with small scan slices, we introduce the ensemble Kalman filter, a kind of Monte Carlo-based Bayesian filter. The proposed method makes it possible to perform odometry with only a very narrow field of view (FoV), and the robustness to point cloud distortion is improved. We demonstrate the effectiveness of the proposed method through Monte Carlo simulations and several tests with fast-moving scenarios. The experimental results prove the possibility of odometry with a very narrow FoV of down to 10 degrees and robustness against motion distortion.