Integrated Object Segmentation and Tracking for 3D LIDAR Data

This paper proposes a novel method for integrated tracking and segmentation of 3D Light Detection and Ranging (LIDAR) data. The conventional processing pipeline of object tracking methods performs the segmentation and tracking modules consecutively. They apply a connected component algorithm on a grid for object segmentation. This results in an under-segmentation and in turn wrong tracking estimates when there are spatially close objects. We present a new approach in which segmentation and tracking modules profit from each other to resolve ambiguities in complex dynamic scenes. A non-parametric Bayesian method, the sequential distance dependent Chinese Restaurant Process (s-ddCRP), enables us to combine segmentation and tracking components. After a pre-processing step which maps measurements to a grid representation, the proposed method tracks each grid cell and segments the environment in an integrated way. A smoothing algorithm is applied to the estimated grid cell velocities for better motion consistency of neighboring dynamic grid cells. Experiments on data obtained with a Velodyne HDL64 scanner in real traffic scenarios illustrate that the proposed approach has a encouraging detection performance and conclusive motion consistency between consecutive time frames.