Abstract
Automatic obstacle detection and avoidance is a key component for the success of micro-aerial vehicles (MAVs) in the future. As the payload of MAVs is highly constrained, cameras are attractive sensors because they are both lightweight and provide rich information about the environment. In this paper, we present an approach that allows a quadrotor with a single monocular camera to locally generate collision-free waypoints. We acquire a small set of images while the quadrotor is hovering from which we compute a dense depth map. Based on this depth map, we render a 2D scan and generate a suitable waypoint for navigation. In our experiments, we found that the pose variation during hovering is already sufficient to obtain suitable depth maps. The computation takes less than one second which renders our approach applicable for obstacle avoidance in real-time. We demonstrate the validity of our approach in challenging environments where we navigate a Parrot Ardrone quadrotor successfully through narrow passages including doors, boxes, and people.
This work was partially supported by the German Academic Exchange Service (DAAD), the DFG under contract number FO 180/17-1 in the Mapping on Demand (MOD) project and the Ministerio de Economía y Competitividad under project DPI2012-36070: Semantic and Active SLAM for heterogeneous systems.
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Notes
- 1.
http://wiki.ros.org/tum_ardrone.
We would like to thank Jakob Engel for his support and advice on using the tum_ardrone package.
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Acknowledgments
The authors would like to thank Pedro Piniés for the so many fruitful discussions about convex optimisation and variational methods. His insights about saddle point methods acquired during his stay in Graz University of Technology was crucial to understand and implement the core of the energy minimisation with TV regularisation.
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Alvarez, H., Paz, L.M., Sturm, J., Cremers, D. (2016). Collision Avoidance for Quadrotors with a Monocular Camera. In: Hsieh, M., Khatib, O., Kumar, V. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 109. Springer, Cham. https://doi.org/10.1007/978-3-319-23778-7_14
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