D²-LIO：考虑方向退化的激光雷达-惯性测量单元里程计增强优化
D$^2$-LIO: Enhanced Optimization for LiDAR-IMU Odometry Considering Directional Degeneracy
AI总结：
针对激光雷达特征退化导致LIO状态估计不可靠的问题，提出集成自适应抗外点匹配与扫描到子图配准的增强框架，通过动态阈值、IMU辅助配准及融合协方差与退化度量的加权矩阵，在稀疏/退化环境中鲁棒性与精度优于现有方法。
摘要：
激光雷达-惯性测量单元里程计（LIO）在实现精确的定位与建图中至关重要，尤其在复杂环境中。然而，激光雷达特征退化的存在对可靠的状态估计构成重大挑战。为解决此问题，我们提出一种增强型LIO框架，将自适应抗外点匹配与扫描到子图配准策略相结合。核心贡献在于自适应外点移除阈值，该阈值基于点到传感器的距离和平台的运动幅度动态调整，此机制提升了不同条件下特征匹配的鲁棒性。此外，我们引入灵活的扫描到子图配准方法，利用IMU数据优化位姿估计，尤其在几何退化配置中。为进一步提高定位精度，我们设计了一种新的加权矩阵，融合IMU预积分协方差与扫描到子图过程中导出的退化度量。在室内外稀疏或退化特征环境中进行的大量实验表明，我们的方法在鲁棒性和精度上持续优于最先进的方法。
英文摘要：
LiDAR-inertial odometry (LIO) plays a vital role in achieving accurate localization and mapping, especially in complex environments. However, the presence of LiDAR feature degeneracy poses a major challenge to reliable state estimation. To overcome this issue, we propose an enhanced LIO framework that integrates adaptive outlier-tolerant correspondence with a scan-to-submap registration strategy. The core contribution lies in an adaptive outlier removal threshold, which dynamically adjusts based on point-to-sensor distance and the motion amplitude of platform. This mechanism improves the robustness of feature matching in varying conditions. Moreover, we introduce a flexible scan-to-submap registration method that leverages IMU data to refine pose estimation, particularly in degenerate geometric configurations. To further enhance localization accuracy, we design a novel weighting matrix that fuses IMU preintegration covariance with a degeneration metric derived from the scan-to-submap process. Extensive experiments conducted in both indoor and outdoor environments-characterized by sparse or degenerate features-demonstrate that our method consistently outperforms state-of-the-art approaches in terms of both robustness and accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
