A LRF and stereovision based data association method for objects tracking

This paper presents a fusion method for objects tracking using laser sensory data and stereovision. Based on the extended Kalman filter, the tracking uses an oriented bounding box (OBB) representation for tracked objects. The representation model takes into account an inter-rays (IR) uncertainty concept, which is related to the fact that the laser raw data points representing the extremities of an extracted OBB do not coincide with the real objects extremities. To improve the objects state estimation, the tracking process integrates a fixed size (FS) assumption. The FS assumption allows to exploit the most precise object´s size estimation, memorised during the tracking. To achieve data association, a threshold based laser points clustering provides satisfying results. However, there are many cases where, without additional information, it is impossible to cluster laser raw data points correctly. To discard clustering ambiguities, a fusion method combining laser sensory data and stereovision information is proposed. The stereovision information is extracted only within regions of interest, defined from laser points. The fusion method takes place in the early stage of the measurement extraction from laser raw data points. The proposed approach is tested and evaluated to demonstrate its reliability.