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Título: | Searching and tracking people in urban environments with static and dynamic obstacles |
Autor: | Goldhoorn, Alex CSIC ORCID; Garrell, Anaís CSIC ORCID ; Alquézar Mancho, Renato CSIC ; Sanfeliu, Alberto CSIC ORCID | Palabras clave: | Particle filter Human–robot-interaction Urban robotics Search and rescue robots |
Fecha de publicación: | 2017 | Editor: | Elsevier | Citación: | Robotics and Autonomous Systems 98: 147-157 (2017) | Resumen: | Searching 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. | Versión del editor: | https://doi.org/10.1016/j.robot.2017.06.005 | URI: | http://hdl.handle.net/10261/166493 | DOI: | 10.1016/j.robot.2017.06.005 | Identificadores: | doi: 10.1016/j.robot.2017.06.005 issn: 0921-8890 |
Aparece en las colecciones: | (IRII) Artículos |
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searching-obstacle.pdf | 446,35 kB | Adobe PDF | Visualizar/Abrir |
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