The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Receding Horizon "Next-Best-View" Planner for 3D Exploration
Abstract
This paper presents a novel path planning algorithm for the autonomous exploration of unknown space using aerial robotic platforms. The proposed planner employs a receding horizon “next-best-view” scheme: In an online computed random tree it finds the best branch, the quality of which is determined by the amount of unmapped space that can be explored. Only the first edge of this branch is executed at every planning step, while repetition of this procedure leads to complete exploration results. The proposed planner is capable of running online, onboard a robot with limited resources. Its high performance is evaluated in detailed simulation studies as well as in a challenging real world experiment using a rotorcraft micro aerial vehicle. Analysis on the computational complexity of the algorithm is provided and its good scaling properties enable the handling of large scale and complex problem setups.
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Cites
A frontier-based approach for autonomous exploration
Yamauchi, B. (2002) · Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation' · doi:10.1109/cira.1997.613851 · Cited by 1,695
A UAV system for inspection of industrial facilities
Nikolic, J., Burri, M., Rehder, J., Leutenegger, S., Huerzeler, C., & Siegwart, R. (2013) · 2013 IEEE Aerospace Conference · doi:10.1109/aero.2013.6496959 · Cited by 251
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Cited by
weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming
Sa, I., Chen, Z., Popovic, M., Khanna, R., Liebisch, F., Nieto, J., & Siegwart, R. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2774979 · Cited by 369
Self-citationAn Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments
Schmid, L., Pantic, M., Khanna, R., Ott, L., Siegwart, R., & Nieto, J. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2969191 · Cited by 308
Self-citationThe limits and potentials of deep learning for robotics
Sünderhauf, N., Brock, O., Scheirer, W., Hadsell, R., Fox, D., Leitner, J., Upcroft, B., Abbeel, P., Burgard, W., Milford, M., et al. (2018) · The International Journal of Robotics Research · doi:10.1177/0278364918770733 · Cited by 38
E-VAT: An Asymmetric End-to-End Approach to Visual Active Exploration and Tracking
Dionigi, A., Devo, A., Guiducci, L., & Costante, G. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3150866 · Cited by 28
Kulathunga, G., Hamed, H., Devitt, D., & Klimchik, A. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3151157 · Cited by 19
Metadata
- Authors
- Bircher, A., Kamel, M., Alexis, K., Oleynikova, H., & Siegwart, R.
- Venue
- 2016 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2016-05
- Pages
- 1462-1468
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2409009991
- Cited by
- 633
- References
- 2
- FWCI
- 13.63
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Bircher, A., Kamel, M., Alexis, K., Oleynikova, H., & Siegwart, R. (2016). Receding Horizon "Next-Best-View" Planner for 3D Exploration. 2016 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2016.7487281
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