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：同步定位与建图算法在不同环境中的灵活性始终是一项重大挑战。为解决高噪声场景下激光雷达里程计漂移问题，整合聚类方法滤除不稳定特征已成为SLAM框架的有效模块。然而，点云数据量的减少可能导致潜在信息损失和退化风险。为此，本研究提出一种可动态评估点云可靠性的激光雷达里程计算法。该算法通过选取对环境退化程度敏感的关键特征点，提升多场景适应性。首先，提出基于距离图像的快速自适应欧几里得聚类算法，结合深度聚类提取环境主结构点（定义为环境骨架点）；随后通过骨架点的密集法向特征计算环境退化程度，并据此动态调整点云清洗策略。该算法在KITTI基准测试和真实环境中得到验证，在不同场景下均展现出更高的精度与鲁棒性。