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.

翻译：固态激光雷达-惯性SLAM因其在速度和鲁棒性方面的优势而受到广泛关注。然而，在极端环境中实现精确建图仍具有挑战性，原因在于严重的几何退化及不可靠的观测常导致病态优化和地图不一致性。为解决这些问题，本文提出一种环境自适应固态激光雷达-惯性里程计，通过融合局部法向量约束与退化感知地图维护来提升定位精度。具体而言，我们引入局部法向量约束以增强状态估计的稳定性，有效抑制退化场景中的定位漂移。同时，设计了一种退化引导的地图更新策略以提高地图精度。得益于精化的地图表示，后续估计中的定位精度得到进一步提升。实验结果表明，所提方法在极端和感知退化环境中实现了更优的建图精度与鲁棒性，相较于基线方法，平均均方根误差降低高达12.8%。