The flexibility of Simultaneous Localization and Mapping (SLAM) algorithms in various environments has consistently been a significant challenge. To address the issue of LiDAR odometry drift in high-noise settings, integrating clustering methods to filter out unstable features has become an effective module of SLAM frameworks. However, reducing the amount of point cloud data can lead to potential loss of information and possible degeneration. As a result, this research proposes a LiDAR odometry that can dynamically assess the point cloud's reliability. The algorithm aims to improve adaptability in diverse settings by selecting important feature points with sensitivity to the level of environmental degeneration. Firstly, a fast adaptive Euclidean clustering algorithm based on range image is proposed, which, combined with depth clustering, extracts the primary structural points of the environment defined as ambient skeleton points. Then, the environmental degeneration level is computed through the dense normal features of the skeleton points, and the point cloud cleaning is dynamically adjusted accordingly. The algorithm is validated on the KITTI benchmark and real environments, demonstrating higher accuracy and robustness in different environments.

翻译：同时定位与地图构建（SLAM）算法在不同环境中的适应性始终是一项重大挑战。为解决高噪声场景下LiDAR里程计漂移问题，集成聚类方法滤除不稳定特征已成为SLAM框架中的有效模块。然而，减少点云数据量可能导致潜在信息损失和可能的退化。为此，本研究提出一种能够动态评估点云可靠性的LiDAR里程计算法。该算法通过选取对环境退化水平敏感的关键特征点，旨在提升在不同场景中的适应性。首先，提出一种基于距离图像的快速自适应欧氏聚类算法，结合深度聚类提取环境中的主要结构点（定义为环境骨架点）。然后，通过骨架点的密集法线特征计算环境退化水平，并据此动态调整点云清理程度。该算法在KITTI基准数据集和真实环境中进行了验证，结果表明其在多种环境中具有更高的精度和鲁棒性。