Modern robotic systems are required to operate in challenging environments, which demand reliable localization under challenging conditions. LiDAR-based localization methods, such as the Iterative Closest Point (ICP) algorithm, can suffer in geometrically uninformative environments that are known to deteriorate point cloud registration performance and push optimization toward divergence along weakly constrained directions. To overcome this issue, this work proposes i) a robust fine-grained localizability detection module, and ii) a localizability-aware constrained ICP optimization module, which couples with the localizability detection module in a unified manner. The proposed localizability detection is achieved by utilizing the correspondences between the scan and the map to analyze the alignment strength against the principal directions of the optimization as part of its fine-grained LiDAR localizability analysis. In the second part, this localizability analysis is then integrated into the scan-to-map point cloud registration to generate drift-free pose updates by enforcing controlled updates or leaving the degenerate directions of the optimization unchanged. The proposed method is thoroughly evaluated and compared to state-of-the-art methods in simulated and real-world experiments, demonstrating the performance and reliability improvement in LiDAR-challenging environments. In all experiments, the proposed framework demonstrates accurate and generalizable localizability detection and robust pose estimation without environment-specific parameter tuning.

翻译：现代机器人系统需要在具有挑战性的环境中运行，这要求其能够在恶劣条件下实现可靠定位。基于激光雷达的定位方法（如迭代最近点算法，ICP）在几何信息匮乏的环境中容易受到影响——此类环境已知会恶化点云配准性能，并导致优化过程沿弱约束方向发散。为解决该问题，本文提出：i) 一个鲁棒的细粒度可定位性检测模块，以及ii) 一个可定位性感知的约束ICP优化模块，该模块以统一方式与可定位性检测模块耦合。所提出的可定位性检测通过利用扫描与地图之间的对应关系，分析优化主方向上的对齐强度，作为其细粒度激光雷达可定位性分析的一部分。在第二部分中，该可定位性分析被进一步集成到扫描-地图点云配准中，通过强制控制更新或保持优化的退化方向不变，生成无漂移位姿更新。所提出的方法在仿真和真实世界实验中与最先进方法进行了全面评估与对比，证明了其在激光雷达挑战性环境中的性能与可靠性提升。在所有实验中，该框架无需针对特定环境调整参数，即可实现准确且泛化的可定位性检测与鲁棒位姿估计。