The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
CoSLAM: Collaborative Visual SLAM in Dynamic Environments
Abstract
This paper studies the problem of vision-based simultaneous localization and mapping (SLAM) in dynamic environments with multiple cameras. These cameras move independently and can be mounted on different platforms. All cameras work together to build a global map, including 3D positions of static background points and trajectories of moving foreground points. We introduce intercamera pose estimation and intercamera mapping to deal with dynamic objects in the localization and mapping process. To further enhance the system robustness, we maintain the position uncertainty of each map point. To facilitate intercamera operations, we cluster cameras into groups according to their view overlap, and manage the split and merge of camera groups in real time. Experimental results demonstrate that our system can work robustly in highly dynamic environments and produce more accurate results in static environments.
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Cites
Shi, J., & Tomasi (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323794 · Cited by 7,724
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
Simultaneous localization and mapping: part I
Durrant-Whyte, H., & Bailey, T. (2006) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2006.1638022 · Cited by 4,199
MonoSLAM: Real-time single camera SLAM
Davison, A. J., Reid, I. D., Molton, N. D., & Stasse, O. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1049 · Cited by 3,959
Strasdat, H., Montiel, J. M. M., & Davison, A. J. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.02.009 · Cited by 439
Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments
Wolf, D. F., & Sukhatme, G. S. (2005) · Autonomous Robots · doi:10.1007/s10514-005-0606-4 · Cited by 248
A hybrid SLAM representation for dynamic marine environments
Bibby, C., & Reid, I. (2010) · 2010 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2010.5509262 · Cited by 53
Cited by
Visual SLAM and Structure from Motion in Dynamic Environments: A Survey
Saputra, M. R. U., Markham, A., & Trigoni, N. (2019) · ACM Computing Surveys · doi:10.1145/3177853 · Cited by 398
M2DGR: A Multi-Sensor and Multi-Scenario SLAM Dataset for Ground Robots
Yin, J., Li, A., Li, T., Yu, W., & Zou, D. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3138527 · Cited by 299
Self-citationSchmuck, P., & Chli, M. (2019) · Journal of Field Robotics · doi:10.1002/rob.21854 · Cited by 253
A review of research on slam technology based on the fusion of lidar and vision
Chen, P., Zhao, X., Zeng, L., Liu, L., Liu, S., Sun, L., Li, Z., Chen, H., Liu, G., Qiao, Z., et al. (2025) · Sensors · doi:10.3390/s25051447 · Cited by 46
Metadata
- Authors
- Zou, D., & Tan, P.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2013-02
- Volume
- 35
- Issue
- 2
- Pages
- 354-366
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2076917298
- Cited by
- 382
- References
- 7
- FWCI
- 16.07
- Type
- journal article
- Language
- en
- Open access
- closed
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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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