LiDAR odometry is a fundamental component of autonomous robotic systems, relying on geometric registration between consecutive point clouds to estimate ego-motion. However, traditional geometric approaches often degrade in dynamic or unstructured environments due to unreliable correspondences caused by moving objects, sparse geometric features, vegetation, and semantically ambiguous structures. Existing works have shown that, some of these limitations can be addressed by introducing semantic information from the environment in the registration process. In this work, we build on this, and show that not all elements in the environment are equally relevant for registration. Hence, we propose a semantic class-weighted ICP for LiDAR odometry. Instead of strictly filtering out points belonging to specific semantic classes, the proposed approach weights the residuals of points belonging to semantic categories based on their expected geometric stability. This strategy enables informative but potentially unstable structures, to contribute to the registration process while mitigating the influence of dynamic objects. The experimental evaluation was conducted on the SemanticKITTI and RELLIS-3D datasets, which include urban, highway, rural, and off-road environments. The empirical results show that the proposed Semantic-weighted ICP improves pose estimation, especially in challenging off-road scenarios where conventional rigid features are scarce. Furthermore, the analysis reveals that the effectiveness of this weighting strategy is highly environment-dependent, influenced by the structural and semantic composition of the scene.

翻译：激光雷达里程计是自主机器人系统的基础组成部分，依赖连续点云之间的几何配准来估计自运动。然而，传统几何方法在动态或非结构化环境中常常性能下降，这是由于移动物体、稀疏几何特征、植被以及语义模糊结构导致不可靠的对应关系。现有研究表明，通过将环境中的语义信息引入配准过程，可以解决其中部分局限性。本文在此基础上进一步证明，环境中并非所有元素对配准都具有同等重要性。因此，我们提出一种用于激光雷达里程计的语义类别加权ICP方法。该方法并非严格过滤掉属于特定语义类别的点，而是根据其预期的几何稳定性，对属于不同语义类别的点残差进行加权。该策略使信息丰富但可能不稳定的结构能够为配准过程做出贡献，同时降低动态物体的影响。实验评估在SemanticKITTI和RELLIS-3D数据集上进行，涵盖城市、高速公路、乡村及越野环境。实证结果表明，本文提出的语义加权ICP方法能改善位姿估计，特别是在传统刚性特征稀缺的挑战性越野场景中。此外，分析表明该加权策略的有效性高度依赖于环境，受场景的结构和语义组成影响。