The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes
Open accessAbstract
The assumption of scene rigidity is typical in SLAM algorithms. Such a strong assumption limits the use of most visual SLAM systems in populated real-world environments, which are the target of several relevant applications like service robotics or autonomous vehicles. In this letter we present DynaSLAM, a visual SLAM system that, building on ORB-SLAM2, adds the capabilities of dynamic object detection and background inpainting. DynaSLAM is robust in dynamic scenarios for monocular, stereo, and RGB-D configurations. We are capable of detecting the moving objects either by multiview geometry, deep learning, or both. Having a static map of the scene allows inpainting the frame background that has been occluded by such dynamic objects. We evaluate our system in public monocular, stereo, and RGB-D datasets. We study the impact of several accuracy/speed trade-offs to assess the limits of the proposed methodology. DynaSLAM outperforms the accuracy of standard visual SLAM baselines in highly dynamic scenarios. And it also estimates a map of the static parts of the scene, which is a must for long-term applications in real-world environments.
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Cites
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Mur-Artal, R., & Tardos, J. D. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2705103 · Cited by 6,898 · cited 6×
“All the methods –both feature-based and direct ones– that map the static scene parts only from the information contained in the sequence [1], [3], [9], [12]–[17], fail to estimate lifelong models when an a priori dynamic object remains static, e.”
BackgroundMethodInfluentialORB-SLAM: A Versatile and Accurate Monocular SLAM System
Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2463671 · Cited by 6,778
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691
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
LSD-SLAM: Large-scale direct monocular SLAM
Engel, J., Schöps, T., & Cremers, D. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10605-2_54 · Cited by 3,851
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2844175 · Cited by 3,457
DTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078
He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017) · arXiv · doi:10.48550/arxiv.1703.06870 · Cited by 406
Cited by
Xiao, L., Wang, J., Qiu, X., Rong, Z., & Zou, X. (2019) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2019.03.012 · Cited by 348
YOLO-SLAM: A semantic SLAM system towards dynamic environment with geometric constraint
Wu, W., Guo, L., Gao, H., You, Z., Liu, Y., & Chen, Z. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06764-3 · Cited by 266
RGB-D SLAM in Dynamic Environments Using Point Correlations
Dai, W., Zhang, Y., Li, P., Fang, Z., & Scherer, S. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3010942 · Cited by 265
Tsintotas, K. A., Bampis, L., & Gasteratos, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3175656 · Cited by 175
“as the vast majority of the approaches and datasets assume a static environment which limits the applicability of visual SLAM in many relevant cases, such as intelligent autonomous systems operating in populated real-world environments [305], [306].”
BackgroundA Survey on Visual Navigation and Positioning for Autonomous UUVs
Qin, J., Li, M., Li, D., Zhong, J., & Yang, K. (2022) · Remote Sensing · doi:10.3390/rs14153794 · Cited by 65
Metadata
- Authors
- Bescos, B., Facil, J. M., Civera, J., & Neira, J.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2018-10
- Volume
- 3
- Issue
- 4
- Pages
- 4076-4083
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2808571300
- Cited by
- 1,263
- References
- 9
- FWCI
- 39.86
- Type
- journal article
- Language
- en
- Open access
- green
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- Bescos, B., Facil, J. M., Civera, J., & Neira, J. (2018). DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2018.2860039
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