Solid-state LiDARs are more compact and cheaper than the conventional mechanical multi-line spinning LiDARs, which have become increasingly popular in autonomous driving recently. However, there are several challenges for these new LiDAR sensors, including severe motion distortions, small field of view and sparse point cloud, which hinder them from being widely used in LiDAR odometry. To tackle these problems, we present an effective continuous-time LiDAR odometry (ECTLO) method for the Risley prism-based LiDARs with non-repetitive scanning patterns. To account for the noisy data, a filter-based point-to-plane Gaussian Mixture Model is used for robust registration. Moreover, a LiDAR-only continuous-time motion model is employed to relieve the inevitable distortions. To facilitate the implicit data association in parallel, we maintain all map points within a single range image. Extensive experiments have been conducted on various testbeds using the solid-state LiDARs with different scanning patterns, whose promising results demonstrate the efficacy of our proposed approach.
翻译:固态激光雷达比传统机械多线旋转激光雷达更加紧凑、更便宜,而传统机械多线旋转激光雷达最近在自主驾驶中越来越受欢迎。然而,这些新的激光雷达传感器面临若干挑战,包括严重的运动扭曲、小视野和零星的点云,这使它们无法被广泛用于激光雷达测量系统。为了解决这些问题,我们为以非重复扫描模式的Risley光谱旋转激光雷达系统提供了一种有效的连续时间LIDAR测量法(ECTLLO ) 。为了说明噪音数据,采用了基于过滤的点对平面的高山混合模型来进行有力的登记。此外,为了减轻不可避免的扭曲,还采用了激光雷达只使用连续时间运动模型。为了便利隐含的数据联系,我们同时在单一范围图像中保留了所有的地图点。已经对使用具有不同扫描模式的固态激光雷达的各种试验台进行了广泛的实验,这些试验有希望的结果显示我们拟议方法的功效。