Two laser scanners raw sensory data fusion for objects tracking using Inter-Rays uncertainty and a Fixed Size assumption

This paper presents a fusion method for objects tracking using two laser range finders (LRF). The tracking is based on the extended Kalman filter. Tracked objects are represented by oriented bounding box (OBB). To improve the objects state estimation, two paradigms are introduced. The first one concerns inter rays (IR) uncertainty, which considers the fact that the raw data points representing the extremities of an extracted OBB do not coincide with the real objects extremities. The second paradigm, called fixed size assumption, assumes that objects size does not change during the tracking. This is expressed by the fact that a track representing an object change its size depending on the IR uncertainty. The fusion technique benefits of the increased perception angular resolution obtained by using two LRFs. The fusion technique takes place in the early stage of the measurement extraction from the raw data points. Experimental results are presented to demonstrate the reliability of the two LRF based fusion method, especially for far objects.