ALO: An Adaptive LiDAR Odometry Approach for Dynamic Environments

Light Detection and Ranging (LiDAR) odometry is a critical technology widely applied in pose estimation for autonomous driving and in Simultaneous Localization and Mapping (SLAM). By using a laser scanner, LiDAR captures environmental information to enable precise spatial localization and mapping. However, traditional LiDAR odometry methods mainly depend on static environmental features for positioning and mapping, limiting adaptability in dynamic settings and reducing pose estimation accuracy. To overcome this limitation, we propose ALO, a novel adaptive LiDAR odometry approach designed for dynamic environments. First, an adaptive constant velocity model predicts the expected motion trajectory, supplying prior pose information, while a first-in-first-out voxel grid manages the local map in dynamic conditions. Next, a linear system with dynamic weights based on point-surface residuals is established, minimizing the influence of dynamic features on pose estimation. Finally, the predicted prior pose serves as the initial value for adaptive Iterative Closest Point (ICP) registration, enhancing pose estimation accuracy and enabling real-time local map updates. Extensive experiments on the public KITTI dataset demonstrate that the proposed method achieves at least a 23.69% improvement in pose estimation accuracy over existing solutions.