The capability of a robot to follow autonomously a person highly enhances its usability when humans and robots collaborate. In this paper we present a system for autonomous following of a walking person in outdoor environments while avoiding static and dynamic obstacles. The principal sensor is a 3D LIDAR with a resolution of 59x29 points. We present a combination of 3D features, motion detection and tracking with a sampling Bayesian filter which results in reliable person detection for a low-resolution 3D-LIDAR. The method is implemented on an outdoor robot with car-like steering, which incorporates the target's path into its own path planning around local obstacles. Experiments in outdoor areas validate the approach.
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@inproceedings{bohlmann2012pemra, author = {Karsten Bohlmann and Andreas Beck-Greinwald and Sebastian Buck and Henrik Marks and Andreas Zell }, title = { Autonomous Person Following with 3D LIDAR in Outdoor Environments}, booktitle = {1st International Workshop on Perception for Mobile Robots Autonomy (PEMRA 2012)}, year = {2012}, address = {Poznan, Poland}, month = sep, abstract = {The capability of a robot to follow autonomously a person highly enhances its usability when humans and robots collaborate. In this paper we present a system for autonomous following of a walking person in outdoor environments while avoiding static and dynamic obstacles. The principal sensor is a 3D LIDAR with a resolution of 59x29 points. We present a combination of 3D features, motion detection and tracking with a sampling Bayesian filter which results in reliable person detection for a low-resolution 3D-LIDAR. The method is implemented on an outdoor robot with car-like steering, which incorporates the target's path into its own path planning around local obstacles. Experiments in outdoor areas validate the approach.}, affiliation = {Chair of Cognitive Systems, University of Tuebingen, Department of Computer Science, Sand 1, 72076 Tuebingen, Germany}, url = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2012/bohlmann2012pemra.pdf} }