Fast Frontier-based Information-driven Autonomous Exploration with an MAV
| Autor: | Dai, Anna, Papatheodorou, Sotiris, Funk, Nils, Tzoumanikas, Dimos, Leutenegger, Stefan |
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| Rok vydání: | 2020 |
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| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/ICRA40945.2020.9196707 |
| Popis: | Exploration and collision-free navigation through an unknown environment is a fundamental task for autonomous robots. In this paper, a novel exploration strategy for Micro Aerial Vehicles (MAVs) is presented. The goal of the exploration strategy is the reduction of map entropy regarding occupancy probabilities, which is reflected in a utility function to be maximised. We achieve fast and efficient exploration performance with tight integration between our octree-based occupancy mapping approach, frontier extraction, and motion planning-as a hybrid between frontier-based and sampling-based exploration methods. The computationally expensive frontier clustering employed in classic frontier-based exploration is avoided by exploiting the implicit grouping of frontier voxels in the underlying octree map representation. Candidate next-views are sampled from the map frontiers and are evaluated using a utility function combining map entropy and travel time, where the former is computed efficiently using sparse raycasting. These optimisations along with the targeted exploration of frontier-based methods result in a fast and computationally efficient exploration planner. The proposed method is evaluated using both simulated and real-world experiments, demonstrating clear advantages over state-of-the-art approaches. Comment: Accepted in the International Conference on Robotics and Automation (ICRA) 2020, 7 pages, 8 figures, for the accompanying video see https://youtu.be/tH2VkVony38 |
| Databáze: | arXiv |
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