ObsGuide: A Plug-and-Play Observability-Guided Residual Selection Method for Accurate LiDAR Odometry

Abstract

Light detection and ranging (LiDAR)-based odometry and mapping is a cornerstone of robotic perception and navigation. Recent work has primarily improved the accuracy of LiDAR odometry either by feeding every available residual into the optimizer or by resorting to multisensor fusion; however, the intrinsic information contained in LiDAR residuals has been little explored from the perspective of nonlinear optimization. To address this, we propose observability-guided residual selection (ObsGuide), a novel plug-and-play residual selection method. Rather than developing a standalone system, ObsGuide is designed as a versatile front-end module that seamlessly integrates into existing LiDAR odometry pipelines. It employs a generalized residual evaluation strategy that ranks residuals based on their observability contribution to the six-degree-of-freedom (6-DoF) pose, explicitly accounting for the planarity or linearity of geometric features. By actively retaining only the minimal subset of residuals that impose the strongest pose constraints during optimization, ObsGuide enables existing pipelines to achieve higher accuracy with significantly fewer residuals. Extensive experiments—conducted by integrating ObsGuide into standard baselines (both optimization- and filter-based) across public benchmarks and real-world indoor and outdoor sequences—confirm its effectiveness, runtime efficiency, and strong generalization ability.