针对极端环境下的激光惯性里程计漂移问题，提出自适应优化方案。
Environment-Adaptive Solid-State LiDAR-Inertial Odometry
- 引入局部法向量约束提升状态估计稳定性
- 通过退化感知地图更新策略降低12.8%平均误差
- 适合复杂场景下高精度定位系统研发者
固态激光雷达-惯性SLAM因速度与鲁棒性优势受到关注。然而，在极端环境下，严重的几何退化和不可靠观测常导致优化病态及地图不一致。为此，本文提出一种环境自适应固态激光雷达-惯性里程计，结合局部法向量约束与退化感知地图维护机制，提升定位精度。具体地，引入局部法向量约束以增强状态估计稳定性，有效抑制退化场景下的定位漂移；同时设计退化引导的地图更新策略，提升地图精度。得益于优化后的地图表示，后续估计的定位精度进一步提升。实验表明，该方法在极端及感知退化环境中实现更优的映射精度与鲁棒性，相较基线方法平均均方根误差降低达12.8%。
原文摘要 · Abstract (English)
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.
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