Environment-Adaptive Solid-State LiDAR-Inertial Odometry

TL;DR

This paper introduces an environment-adaptive LiDAR-inertial SLAM method that improves localization accuracy and robustness in extreme environments by integrating local normal-vector constraints and degeneracy-aware map maintenance.

Contribution

It proposes novel local normal-vector constraints and a degeneration-guided map update strategy to enhance mapping accuracy and robustness in challenging environments.

Findings

Achieves up to 12.8% RMSE reduction compared to baseline.

Improves stability of state estimation in degenerate scenarios.

Enhances map precision and localization accuracy in extreme environments.

Abstract

Solid-state LiDAR-inertial SLAM has attracted significant attention due to its advantages in speed and robustness. However, achieving accurate mapping in extreme environments remains challenging due to severe geometric degeneracy and unreliable observations, which often lead to ill-conditioned optimization and map inconsistencies. To address these challenges, we propose an environment-adaptive solid-state LiDAR-inertial odometry that integrates local normal-vector constraints with degeneracy-aware map maintenance to enhance localization accuracy. Specifically, we introduce local normal-vector constraints to improve the stability of state estimation, effectively suppressing localization drift in degenerate scenarios. Furthermore, we design a degeneration-guided map update strategy to improve map precision. Benefiting from the refined map representation, localization accuracy is further enhanced in subsequent estimation. Experimental results demonstrate that the proposed method achieves superior mapping accuracy and robustness in extreme and perceptually degraded environments, with an average RMSE reduction of up to 12.8% compared to the baseline method.