The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments
Open accessAbstract
The ability to plan informative paths online is essential to robot autonomy. In particular, sampling-based approaches are often used as they are capable of using arbitrary information gain formulations. However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do not reach global coverage. In this letter, we present a new RRT*-inspired online informative path planning algorithm. Our method continuously expands a single tree of candidate trajectories and rewires nodes to maintain the tree and refine intermediate paths. This allows the algorithm to achieve global coverage and maximize the utility of a path in a global context, using a single objective function. We demonstrate the algorithm's capabilities in the applications of autonomous indoor exploration as well as accurate Truncated Signed Distance Field (TSDF)-based 3D reconstruction on-board a Micro Aerial Vehicle (MAV). We study the impact of commonly used information gain and cost formulations in these scenarios and propose a novel TSDF-based 3D reconstruction gain and cost-utility formulation. Detailed evaluation in realistic simulation environments show that our approach outperforms sampling-based state of the art methods in these tasks. Experiments on a real MAV demonstrate the ability of our method to robustly plan in real-time, exploring an indoor environment with on-board sensing and computation. We make our framework available for future research.
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Cites
Marching cubes: A high resolution 3D surface construction algorithm
Lorensen, W. E., & Cline, H. E. (1987) · Proceedings of the 14th annual conference on Computer graphics and interactive techniques · doi:10.1145/37401.37422 · Cited by 10,104
Marching cubes: A high resolution 3D surface construction algorithm
Lorensen, W. E., & Cline, H. E. (1987) · ACM SIGGRAPH Computer Graphics · doi:10.1145/37402.37422 · Cited by 8,647
Sampling-based algorithms for optimal motion planning
Karaman, S., & Frazzoli, E. (2011) · The International Journal of Robotics Research · doi:10.1177/0278364911406761 · Cited by 5,275
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
Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments
Richter, C., Bry, A., & Roy, N. (2016) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-319-28872-7_37 · Cited by 747
Voxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MAV planning
Oleynikova, H., Taylor, Z., Fehr, M., Siegwart, R., & Nieto, J. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202315 · Cited by 658
Self-citationReceding Horizon "Next-Best-View" Planner for 3D Exploration
Bircher, A., Kamel, M., Alexis, K., Oleynikova, H., & Siegwart, R. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487281 · Cited by 633
Self-citationRotorS—A Modular Gazebo MAV Simulator Framework
Furrer, F., Burri, M., Achtelik, M., & Siegwart, R. (2016) · Studies in Computational Intelligence · doi:10.1007/978-3-319-26054-9_23 · Cited by 592
Self-citationIterated extended Kalman filter based visual-inertial odometry using direct photometric feedback
Bloesch, M., Burri, M., Omari, S., Hutter, M., & Siegwart, R. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917728574 · Cited by 485
Self-citationIntel(R) RealSense(TM) Stereoscopic Depth Cameras
Keselman, L., Woodfill, J. I., Grunnet-Jepsen, A., & Bhowmik, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.167 · Cited by 448
Cited by
Tutsoy, O., Asadi, D., Ahmadi, K., Nabavi-Chashmi, S. Y., & Iqbal, J. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3367769 · Cited by 98
Metadata
- Authors
- Schmid, L., Pantic, M., Khanna, R., Ott, L., Siegwart, R., & Nieto, J.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2020-04
- Volume
- 5
- Issue
- 2
- Pages
- 1500-1507
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2973864946
- Cited by
- 308
- References
- 28
- FWCI
- 13.14
- Type
- journal article
- Language
- en
- Open access
- green
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- Schmid, L., Pantic, M., Khanna, R., Ott, L., Siegwart, R., & Nieto, J. (2020). An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2020.2969191
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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