Abstract: Systems, methods, and other embodiments described herein relate to detecting reflection artifacts in a point cloud. In one embodiment, a method includes generating a first convex hull for defining an outline of a first cluster of points in a depth image and generating a second convex hull for defining an outline of a second cluster of points in the depth image. The first cluster of points originates from a point cloud and the second cluster of points originates from the point cloud. The method includes determining whether the second cluster of points is a reflection artifact based on identifying if a portion of the second convex hull is enclosed in the first convex hull and a range of the second convex hull is larger than a range of the first convex hull.

Abstract: The technology relates to tracking objects in an environment around an autonomous vehicle. A computing system of the autonomous vehicle determines accurate motion characteristics of objects detected in its environment despite various sensor measurement limitations. By correcting motion distortion for fast moving objects and accounting for discrepancies in sensor data gathering, motion characteristics may be determined for the detected objects with enhanced accuracy. Multiple sets of correspondences are determined for clusters from multiple sensor spins, enabling better alignment using a surface matching algorithm even when clusters have fewer data points. Efficiency is also enhanced by selecting hypotheses based on confidence levels. These techniques provide for identifying the types of objects for which a yaw rate can be accurately determined. Object classification can also be improved by accumulating associated clusters corresponding to a detected object.