Tracking multiple targets from a mobile robot platform using a laser range scanner

A major issue in the field of mobile robotics today is the detection and tracking of moving objects (DATMO) from a moving observer. In dynamic and highly populated environments, this problem presents a complex and computationally demanding task. It can be divided in sub-problems such as robot´s relative motion compensation, feature extraction, measurement clustering, data association and targets´ state vector estimation. In this paper we present an innovative approach that addresses all these issues exploiting various probabilistic and deterministic techniques. The algorithm utilizes real laser-scanner data to dynamically extract moving objects from their background environment, using a time-fading grid map method, and tracks the identified targets employing a joint probabilistic data association with interacting multiple model (JPDA-IMM) algorithm. The resulting technique presents a computationally efficient approach to already existing target-tracking research for real time application scenarios.