Exploration on continuous Gaussian process frontier maps

Publication Type:
Conference Proceeding
Citation:
Proceedings - IEEE International Conference on Robotics and Automation, 2014, pp. 6077 - 6082
Issue Date:
2014-01-01
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© 2014 IEEE. An information-driven autonomous robotic exploration method on a continuous representation of unknown environments is proposed in This paper. The approach conveniently handles sparse sensor measurements To build a continuous model of The environment That exploits structural dependencies without The need To resort To a fixed resolution grid map. A gradient field of occupancy probability distribution is regressed from sensor data as a Gaussian process providing frontier boundaries for further exploration. The resulting continuous global frontier surface completely describes unexplored regions and, inherently, provides an automatic stop criterion for a desired sensitivity. The performance of The proposed approach is evaluated Through simulation results in The well-known Freiburg and Cave maps.
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