Monte Carlo based distance dependent Chinese restaurant process for segmentation of 3D LIDAR data using motion and spatial features

This paper proposes a novel method to obtain robust and accurate object segmentations from 3D Light Detection and Ranging (LIDAR) data points. The method exploits motion information simultaneously estimated by a tracking algorithm in order to resolve ambiguities in complex dynamic scenes. Typical approaches for tracking multiple objects in LIDAR data follow three steps; point cloud segmentation, object tracking, and track classification. A large number of errors is due to failures in the segmentation component, mainly because segmentation and tracking are performed consecutively and the segmentation step solely relies on geometrical features. This article presents a 3D LIDAR based object segmentation method that exploits the motion information provided by a tracking algorithm and spatial features in order to discriminate spatially close objects. After a pre-processing step that maps LIDAR measurements to an occupancy grid representation, the motions of grid cells are estimated using independent Kalman filters. A distance dependent Chinese Restaurant Process based Markov chain Monte Carlo approach is applied to generate different segmentation hypotheses and decide on the most probable segments by using motion and spatial features together.