Prism-based LiDARs are more compact and cheaper than the conventional mechanical multi-line spinning LiDARs, which have become increasingly popular in robotics, recently. However, there are several challenges for these new LiDAR sensors, including small field of view, severe motion distortions, and irregular patterns, which hinder them from being widely used in LiDAR odometry, practically. 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. A single range image covering historical points in LiDAR's small FoV is adopted for efficient map representation. To account for the noisy data from occlusions after map updating, 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. Extensive experiments have been conducted on various testbeds using the prism-based LiDARs with different scanning patterns, whose promising results demonstrate the efficacy of our proposed approach.

翻译：基于棱镜的激光雷达相较传统机械式多线旋转激光雷达更加紧凑且成本更低，近年来在机器人领域日益普及。然而，这类新型激光雷达传感器存在视场角小、运动畸变严重、扫描模式不规则等挑战，实际限制了其在激光雷达里程计中的广泛应用。为解决上述问题，我们针对基于里奇-棱镜结构的非重复扫描模式激光雷达，提出了一种高效连续时间激光雷达里程计方法。该方法采用覆盖激光雷达小视场历史点云数据的单帧距离图像实现高效地图表征；针对地图更新后遮挡导致的噪声数据，引入基于滤波的点-面高斯混合模型进行鲁棒配准；同时采用纯激光雷达连续时间运动模型以缓解不可避免的畸变问题。基于不同扫描模式棱镜激光雷达的多个测试平台开展了大量实验，实验结果充分验证了所提方法的有效性。