Light detection and ranging (LiDAR)-based odometry has been widely utilized for pose estimation due to its use of high-accuracy range measurements and immunity to ambient light conditions. However, the performance of LiDAR odometry varies depending on the environment and deteriorates in degenerative environments such as long corridors. This issue stems from the dependence on a single error metric, which has different strengths and weaknesses depending on the geometrical characteristics of the surroundings. To address these problems, this study proposes a novel iterative closest point (ICP) method called GenZ-ICP. We revisited both point-to-plane and point-to-point error metrics and propose a method that leverages their strengths in a complementary manner. Moreover, adaptability to diverse environments was enhanced by utilizing an adaptive weight that is adjusted based on the geometrical characteristics of the surroundings. As demonstrated in our experimental evaluation, the proposed GenZ-ICP exhibits high adaptability to various environments and resilience to optimization degradation in corridor-like degenerative scenarios by preventing ill-posed problems during the optimization process.

翻译：基于激光雷达（LiDAR）的里程计因其利用高精度测距数据且不受环境光照条件影响的特性，已被广泛用于位姿估计。然而，激光雷达里程计的性能随环境变化，并在诸如长走廊等退化环境中表现恶化。此问题源于对单一误差度量的依赖，而该度量依据周围环境的几何特性具有不同的优缺点。为解决这些问题，本研究提出了一种新颖的迭代最近点（ICP）方法，称为GenZ-ICP。我们重新审视了点对面和点对点两种误差度量，并提出了一种以互补方式利用其各自优势的方法。此外，通过利用一个根据周围环境几何特性进行调整的自适应权重，增强了对多样化环境的适应性。正如我们的实验评估所展示的，所提出的GenZ-ICP通过防止优化过程中的病态问题，对各种环境表现出高度的适应性，并在类似走廊的退化场景中对优化退化具有鲁棒性。