In human–robot interaction developments, detection, tracking and identification of moving objects (DATMO) constitute an important problem. More specifically, in mobile robots this problem becomes harder and more computationally expensive as the environments become dynamic and more densely populated. The problem can be divided into a number of sub-problems, which include the compensation of the robot's motion, measurement clustering, feature extraction, data association, targets' trajectory estimation and finally, target classification. Here, a mobile robot uses 2D laser range data to identify and track moving targets. A Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) tracking algorithm associates the available laser data to track and provide an estimated state vector of targets' position and velocity. Potential moving objects are initially learned in a supervised manner and later on are autonomously classified in real-time using a trained Fuzzy ART neural network classifier. The recognized targets are fed back to the tracker to further improve the track initiation process. The resulting technique introduces a computationally efficient approach to already existing target-tracking and identification research, which is especially suited for real time application scenarios.
Efficient Planar Surface-Based 3D Mapping Method for Mobile Robots Using Stereo Vision
