Abstract
We propose a novel 3D navigation system for autonomous vehicle path-planning. The system processes a point-cloud data from an RGB-D camera and creates a 3D occupancy grid with adaptable cell size. Occupied grid cells contain normal distribution characterizing the data measured in the area of the cell. The normal distributions are then used for cell classification, traversability, and collision checking. The space of traversable cells is used for path-planning. The ability to work in three-dimensional space allows autonomous robots to operate in highly structured environments with multiple levels, uneven surfaces, and various elevated and underground crossings. That is important for the usage of robots in real-world scenarios such as in urban areas and for disaster rescue missions.
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Acknowledgements
We would like to thank Dr. Ohya from University of Tsukuba, Japan, for his valuable insight that helped with this work and Dr. Obdžálek for help with the robotic platform. Research is supported by the Czech Science Foundation under the contract P103-15-19877S.
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Škoda, J., Barták, R. (2018). 3D Navigation for a Mobile Robot. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 694. Springer, Cham. https://doi.org/10.1007/978-3-319-70836-2_29
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DOI: https://doi.org/10.1007/978-3-319-70836-2_29
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