Abstract
Obstacle avoidance is an essential capability for micro air vehicles. Prior approaches have mainly been either purely reactive, mapping low-level visual features directly to headings, or deliberative methods that use onboard 3-D sensors to create a 3-D, voxel-based world model, then generate 3-D trajectories and check them for potential collisions with the world model. Onboard 3-D sensor suites have had limited fields of view. We use forward-looking stereo vision and lateral structure from motion to give a very wide horizontal and vertical field of regard. We fuse depth maps from these sources in a novel robot-centered, cylindrical, inverse range map we call an egocylinder. Configuration space expansion directly on the egocylinder gives a very compact representation of visible freespace. This supports very efficient motion planning and collision-checking with better performance guarantees than standard reactive methods. We show the feasibility of this approach experimentally in a challenging outdoor environment.
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Acknowledgments
This work was funded by the Army Research Laboratory under the Micro Autonomous Systems & Technology Collaborative Technology Alliance program (MAST-CTA). JPL contributions were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
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Brockers, R., Fragoso, A., Rothrock, B., Lee, C., Matthies, L. (2017). Vision-Based Obstacle Avoidance for Micro Air Vehicles Using an Egocylindrical Depth Map. In: Kulić, D., Nakamura, Y., Khatib, O., Venture, G. (eds) 2016 International Symposium on Experimental Robotics. ISER 2016. Springer Proceedings in Advanced Robotics, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-50115-4_44
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DOI: https://doi.org/10.1007/978-3-319-50115-4_44
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