表征基于体素的激光雷达扫描匹配中的视角误差
Characterizing Perspective Error in Voxel-Based Lidar Scan Matching
AI总结：
本文针对基于体素的激光雷达扫描匹配，建立圆柱柱体与双墙转角的视角误差解析模型，并分析车辆驶过时误差的累积规律。
摘要：
本文量化了一个限制激光雷达扫描匹配精度的误差源，尤其针对基于体素的方法。激光雷达扫描匹配用于航位推算（也称为激光雷达里程计）和建图，计算最佳对齐一对点云的旋转和平移。当从不同角度观察场景时，不同表面在每个视点变得可见或被遮挡，便会产生视角误差。为解释数据中观察到的视角异常，本文针对代表城市景观的两类物体建模视角误差：圆柱柱体和双墙转角。对于每个物体，我们给出了基于体素的激光雷达扫描匹配视角误差的解析模型。随后，我们分析了配备激光雷达的车辆驶过这些物体时视角误差如何累积。
英文摘要：
This paper quantifies an error source that limits the accuracy of lidar scan matching, particularly for voxel-based methods. Lidar scan matching, which is used in dead reckoning (also known as lidar odometry) and mapping, computes the rotation and translation that best align a pair of point clouds. Perspective errors occur when a scene is viewed from different angles, with different surfaces becoming visible or occluded from each viewpoint. To explain perspective anomalies observed in data, this paper models perspective errors for two objects representative of urban landscapes: a cylindrical column and a dual-wall corner. For each object, we provide an analytical model of the perspective error for voxel-based lidar scan matching. We then analyze how perspective errors accumulate as a lidar-equipped vehicle moves past these objects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
