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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Perception-aware Receding Horizon Navigation for MAVs |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Page Range | 1 - 8 |
| Event Title | IEEE International Conference on Robotics and Automation (ICRA), 2018. |
| Event Type | conference |
| Event Location | Brisbane |
| Event Start Date | May 1 - 2018 |
| Event End Date | May 25 - 2018 |
| Place of Publication | IEEE International Conference on Robotics and Automation (ICRA), 2018. |
| Publisher | IEEE |
| Abstract Text | To reach a given destination safely and accurately, a micro aerial vehicle needs to be able to avoid obstacles and minimize its state estimation uncertainty at the same time. To achieve this goal, we propose a perception-aware receding horizon approach. In our method, a single forwardlooking camera is used for state estimation and mapping. Using the information from the monocular state estimation and mapping system, we generate a library of candidate trajectories and evaluate them in terms of perception quality, collision probability, and distance to the goal. The best trajectory to execute is then selected as the one that maximizes a reward function based on these three metrics. To the best of our knowledge, this is the first work that integrates active vision within a receding horizon navigation framework for a goal reaching task. We demonstrate by simulation and real-world experiments on an actual quadrotor that our active approach leads to improved state estimation accuracy in a goal-reaching task when compared to a purely-reactive navigation system, especially in difficult scenes (e.g., weak texture). A video showing the experiments can be found at https://youtu.be/761zxZMeQNo A narrated video presentation can be found here: https://www.youtube.com/watch?v=FK6S_CRXiuI |
| Zusammenfassung | To reach a given destination safely and accurately, a micro aerial vehicle needs to be able to avoid obstacles and minimize its state estimation uncertainty at the same time. To achieve this goal, we propose a perception-aware receding horizon approach. In our method, a single forwardlooking camera is used for state estimation and mapping. Using the information from the monocular state estimation and mapping system, we generate a library of candidate trajectories and evaluate them in terms of perception quality, collision probability, and distance to the goal. The best trajectory to execute is then selected as the one that maximizes a reward function based on these three metrics. To the best of our knowledge, this is the first work that integrates active vision within a receding horizon navigation framework for a goal reaching task. We demonstrate by simulation and real-world experiments on an actual quadrotor that our active approach leads to improved state estimation accuracy in a goal-reaching task when compared to a purely-reactive navigation system, especially in difficult scenes (e.g., weak texture). A video showing the experiments can be found at https://youtu.be/761zxZMeQNo A narrated video presentation can be found here: https://www.youtube.com/watch?v=FK6S_CRXiuI |
| Free access at | Official URL |
| Official URL | http://rpg.ifi.uzh.ch/docs/ICRA18_Zhang.pdf |
| Digital Object Identifier | 10.1109/ICRA.2018.8461133 |
| Other Identification Number | merlin-id:16270 |
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