The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Collaborative monocular SLAM with multiple Micro Aerial Vehicles
Open accessAbstract
This paper presents a framework for collaborative localization and mapping with multiple Micro Aerial Vehicles (MAVs) in unknown environments. Each MAV estimates its motion individually using an onboard, monocular visual odometry algorithm. The system of MAVs acts as a distributed preprocessor that streams only features of selected keyframes and relative-pose estimates to a centralized ground station. The ground station creates an individual map for each MAV and merges them together whenever it detects overlaps. This allows the MAVs to express their position in a common, global coordinate frame. The key to real-time performance is the design of data-structures and processes that allow multiple threads to concurrently read and modify the same map. The presented framework is tested in both indoor and outdoor environments with up to three MAVs. To the best of our knowledge, this is the first work on real-time collaborative monocular SLAM, which has also been applied to MAVs.
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Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Parallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346
Monocular‐SLAM–based navigation for autonomous micro helicopters in GPS‐denied environments
Weiss, S., Scaramuzza, D., & Siegwart, R. (2011) · Journal of Field Robotics · doi:10.1002/rob.20412 · Cited by 414
Self-citationDDF-SAM 2.0: Consistent distributed smoothing and mapping
Cunningham, A., Indelman, V., & Dellaert, F. (2013) · 2013 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2013.6631323 · Cited by 171
Cited by
SVO: Fast semi-direct monocular visual odometry
Forster, C., Pizzoli, M., & Scaramuzza, D. (2014) · 2014 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2014.6906584 · Cited by 2,205
Self-citationMultiple-Robot Simultaneous Localization and Mapping: A Review
Saeedi, S., Trentini, M., Seto, M., & Li, H. (2016) · Journal of Field Robotics · doi:10.1002/rob.21620 · Cited by 324
C2TAM: A Cloud framework for cooperative tracking and mapping
Riazuelo, L., Civera, J., & Montiel, J. M. M. (2014) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2013.11.007 · Cited by 254
Schmuck, P., & Chli, M. (2019) · Journal of Field Robotics · doi:10.1002/rob.21854 · Cited by 253
Edge-SLAM: edge-assisted visual simultaneous localization and mapping
Ben Ali, A. J., Hashemifar, Z. S., & Dantu, K. (2020) · Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3386901.3389033 · Cited by 46
Metadata
- Authors
- Forster, C., Lynen, S., Kneip, L., & Scaramuzza, D.
- Venue
- 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
- Publisher
- IEEE
- Published
- 2013-11
- Pages
- 3962-3970
- OpenAlex
- W2037971631
- Cited by
- 201
- References
- 5
- FWCI
- 21.24
- Type
- conference paper
- Language
- en
- Open access
- green
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