Geometric-Model-Free Tracking of Extended Targets Using 3D-LIDAR-Measurements

Tracking of extended targets in high definition, 360-degree 3D-LIDAR (Light Detection and Ranging) measurements is a challenging task and a current research topic. It is a key component in robotic applications, and is relevant to path planning and collision avoidance. This paper proposes a new method without a geometric model to simultaneously track and accumulate 3D-LIDAR measurements of an object. The method itself is based on a particle filter and uses an object-related local 3D grid for each object. No geometric object hypothesis is needed. Accumulation allows coping with occlusions. The prediction step of the particle filter is governed by a motion model consisting of a deterministic and a probabilistic part. Since this paper is focused on tracking ground vehicles, a bicycle model is used for the deterministic part. The probabilistic part depends on the current state of each particle. A function for calculating the current probability density function for state transition is developed. It is derived in detail and based on a database consisting of vehicle dynamics measurements over several hundreds of kilometers. The adaptive probability density function narrows down the gating area for measurement data association. The second part of the proposed method addresses weighting the particles with a cost function. Different 3D-griddependent cost functions are presented and evaluated. Evaluations with real 3D-LIDAR measurements show the performance of the proposed method. The results are also compared to ground truth data.