Adaptive Sliding Window Optimization for Multi-Modal LiDAR Inertial Odometry and Mapping

Abstract

Fixed-Lag smoothing is widely employed as a backend in localization tasks. Generally, increasing the window length leads to better accuracy, but demands more computational resources. Therefore, determining an appropriate window length and whether a fixed length should be maintained throughout the localization process are worth studying. Assuming independent and identically distributed noise based on the distance-independent characteristic of LiDAR ranging errors, we propose an uncertainty-based adaptive sliding window (ASW) strategy. Through mathematical derivation, the reference uncertainty is affected by the LiDAR feature distribution of each frame. Consequently, we develop a multimodal LiDAR inertial odometry and mapping framework based on ASW, which integrates mechanical and solid-state LiDAR to enhance odometry accuracy and mapping density. By designing a joint matching module, our approach leverages the strengths of distinct scanning patterns. Additionally, we incorporate loop closure detection in the mapping process to minimize cumulative drift. Extensive experiments conducted on both public and self-collected datasets demonstrate the effectiveness of our method. Compared to the state-of-the-art method, our approach improves the average accuracy by 10.3%. We also provide an open-source implementation for further studies. https://github.com/wowhhhhgd/ASW-LIOM.