Integrate Depth Information to Enhance the Robustness of Object Level SLAM
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50075-6_15
Reference15 articles.
1. Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: an open-source slam system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017)
2. Engel, J., Sturm, J., Cremers, D.: Semi-dense visual odometry for a monocular camera. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1449–1456 (2013). https://doi.org/10.1109/iccv.2013.183
3. Chen, X., Milioto, A., Palazzolo, E., Giguère, P., Behley, J., Stachniss, C.: SuMa++: efficient lidar-based semantic SLAM. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4530–4537 (2019). https://doi.org/10.1109/IROS40897.2019.8967704
4. Zhu, Z., et al.: NICE-SLAM: neural implicit scalable encoding for SLAM. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12786–12796 (2022)
5. Zhu, Z., et al.: NICER-SLAM: neural implicit scene encoding for RGB SLAM. arXiv preprint arXiv:2302.03594 (2023)
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