Fast and Versatile Feature-Based LiDAR Odometry via Efficient Local Quadratic Surface Approximation

We present a fast and versatile feature-based LiDAR odometry method using local quadratic surface approximation and point-to-surface alignment. Unlike most feature-based methods, our approach approximates the local geometry of the LiDAR scan as a quadratic surface to mitigate performance degradation caused by the inconsistency between the feature class and the map's local geometry. For computational efficiency, we leverage a symmetric objective function to align features on the local surface of the map without requiring time-consuming surface parameter evaluation. Evaluation on the KITTI and Newer College dataset demonstrates that the proposed method performs better than other feature-based methods. In particular, our approach exhibits robust performance in environments where the ambiguity of feature classifications is considerable. In addition, to demonstrate the robustness of the proposed method when LiDAR scans are relatively sparse, we evaluated the proposed method on datasets collected using LiDAR with a relatively small number of scan channels.

Automated summary

We propose a fast and versatile feature-based LiDAR odometry method that uses local quadratic surface approximation and point-to-surface alignment. Our method tackles the inconsistency between feature class and map's local geometry by approximating the local LiDAR scan's geometry as a quadratic surface. By leveraging a symmetric objective function, we achieve computational efficiency without time-consuming surface parameter evaluation. Evaluation on KITTI and Newer College datasets shows that our method outperforms other feature-based methods, especially in environments with considerable feature classification ambiguity. Moreover, we demonstrate the robustness of our method in sparse LiDAR scans with a relatively small number of scan channels.