Abstract
We propose an online 3D sensor-based algorithm for autonomous robot exploration in an indoor setting. Our algorithm consists of two modules, a proactive open space detection module, and a reactive obstacle avoidance module. The former, which is the primary contribution of the paper, is responsible for guiding the robot towards meaningful open spaces based on high level navigation goals. This generally translates to identifying open doors or corridor vanishing points in a typical indoor setting. The latter is a necessary component that enables safe autonomous exploration by preventing the robot from colliding with objects along the moving path. Assuming a 3D range sensor is mounted on the robot, it continues to scan and acquire signal from its surroundings as it explores in an unknown environment. From each 3D scan, the two modules function cooperatively to identify any open spaces and obstacles within the generated point cloud using robust geometric estimation methods. Combination of the two modules provides the basic capability of a autonomous robot to explore an unknown environment freely. Experimental results with the proposed algorithm on both real world and simulated data are promising.
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Acknowledgements
This material is based upon work supported by Defense Advanced Research Projects Agency under contract numbers W31P4Q-08-C-0264 and HR0011-09-C-0001. Any opinions, findings and conclusion or recommendations expressed in this material are those of the author and do not necessarily reflect the view of the Defense Advanced Research Projects Agency. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressly or implied, of the Defense Advanced Research Projects Agency or the U.S. Government.
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Xu, J., Kim, K., Zhang, L., Khosla, D. (2015). 3D Perception for Autonomous Robot Exploration. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_79
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