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.

翻译：激光雷达（LiDAR）被广泛应用于同步定位与建图（SLAM）及自动驾驶领域。在多传感器融合中，激光雷达里程计至关重要。然而，在某些非结构化环境中，由于点云几何特征稀疏，点云配准无法有效约束激光雷达的位姿，从而导致多传感器融合精度退化。为解决此问题，本文提出了一种新颖的实时退化感知与补偿方法。首先，本文引入了物理意义明确的退化因子，可用于量化激光雷达的退化程度。随后，采用基于密度的噪声应用空间聚类（DBSCAN）方法自适应地感知退化，该方法具有良好的环境泛化能力。最后，利用退化感知结果融合激光雷达与惯性测量单元（IMU）数据，从而有效抵抗退化效应。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，并验证了算法对不同环境及激光雷达扫描模式的适应性。