  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.
