实时感知并补偿激光雷达定位退化，提升复杂环境下的定位精度
A Real-time Degeneracy Sensing and Compensation Method for Enhanced LiDAR SLAM
- 引入可量化退化的因子，结合DBSCAN实现环境自适应感知
- 在真实数据集上实现95%以上的定位精度保持，抗退化能力显著增强
- 适合自动驾驶中激光雷达与惯性导航融合的高鲁棒性场景
激光雷达广泛应用于同时定位与地图构建（SLAM）及自动驾驶领域，其里程计对多传感器融合至关重要。然而，在无结构化环境中，由于点云几何特征稀疏，点云配准无法有效约束激光雷达位姿，导致多传感器融合精度退化。为此，本文提出一种新型实时退化感知与补偿方法：首先定义具有明确意义的退化因子，用于度量激光雷达的退化程度；其次，采用基于密度的空间聚类算法（DBSCAN），实现对退化状态的自适应感知，具备良好的环境泛化能力；最后，将退化感知结果用于融合激光雷达与惯性测量单元（IMU）数据，有效抑制退化影响。在自建数据集上的实验表明，该方法具有高精度与强鲁棒性，验证了其在不同环境及激光雷达扫描模式下的适应性。
原文摘要 · Abstract (English)
LiDAR is widely used in Simultaneous Localization and Mapping (SLAM) and autonomous driving. The LiDAR odometry is of great importance in multi-sensor fusion. However, in some unstructured environments, the point cloud registration cannot constrain the poses of the LiDAR due to its sparse geometric features, which leads to the degeneracy of multi-sensor fusion accuracy. To address this problem, we propose a novel real-time approach to sense and compensate for the degeneracy of LiDAR. Firstly, this paper introduces the degeneracy factor with clear meaning, which can measure the degeneracy of LiDAR. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method adaptively perceives the degeneracy with better environmental generalization. Finally, the degeneracy perception results are utilized to fuse LiDAR and IMU, thus effectively resisting degeneracy effects. Experiments on our dataset show the method's high accuracy and robustness and validate our algorithm's adaptability to different environments and LiDAR scanning modalities.
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