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Searching and tracking people in urban environments with static and dynamic obstacles

AuthorsGoldhoorn, Alex ; Garrell, Anaís ; Alquézar Mancho, Renato ; Sanfeliu, Alberto
KeywordsParticle filter
Urban robotics
Search and rescue robots
Issue Date2017
CitationRobotics and Autonomous Systems 98: 147-157 (2017)
AbstractSearching and tracking people in crowded urban areas where they can be occluded by static or dynamic obstacles is an important behavior for social robots which assist humans in urban outdoor environments. In this work, we propose a method that can handle in real-time searching and tracking people using a Highest Belief Particle Filter Searcher and Tracker. It makes use of a modified Particle Filter (PF), which, in contrast to other methods, can do both searching and tracking of a person under uncertainty, with false negative detections, lack of a person detection, in continuous space and real-time. Moreover, this method uses dynamic obstacles to improve the predicted possible location of the person. Comparisons have been made with our previous method, the Adaptive Highest Belief Continuous Real-time POMCP Follower, in different conditions and with dynamic obstacles. Real-life experiments have been done during two weeks with a mobile service robot in two urban environments of Barcelona with other people walking around.
Publisher version (URL)https://doi.org/10.1016/j.robot.2017.06.005
Identifiersdoi: 10.1016/j.robot.2017.06.005
issn: 0921-8890
Appears in Collections:(IRII) Artículos
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