- Wu Y, Zhang Y, Zhu D, et al. EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020: 4966-4973. [PDF] [YouTube] [bilibili] [Project page].
- Extended Work
- Wu Y, Zhang Y, Zhu D, et al. Object-Driven Active Mapping for More Accurate Object Pose Estimation and Robotic Grasping[J]. arXiv preprint arXiv:2012.01788, 2020. [PDF] [Project page].
- Robotic Grasping demo: YouTube | bilibili
- Augmented Reality demo: YouTube | bilibili
- If you use the code in your academic work, please cite the above paper.
- Prerequisites are the same as semidense-lines. If compiling problems met, please refer to semidense-lines and ORB_SLAM2.
- The code is tested in Ubuntu 16.04, opencv 3.2.0/3.3.1, Eigen 3.2.1.
chmod +x build.sh
- This is an incomplete version of our paper. If you want to use it in your work or with other datasets, you should prepare the offline semantic detection/segmentation results or switch to online mode. Besides, you may need to adjust the data association strategy and abnormal object elimination mechanism (We found the misdetection from YOLO has a great impact on the results).
Thanks for the great work: ORB-SLAM2, Cube SLAM, and Semidense-Lines.
- Mur-Artal R, Tardós J D. Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras[J]. IEEE Transactions on Robotics, 2017, 33(5): 1255-1262. PDF, Code
- Yang S, Scherer S. Cubeslam: Monocular 3-d object slam[J]. IEEE Transactions on Robotics, 2019, 35(4): 925-938. PDF, Code
- He S, Qin X, Zhang Z, et al. Incremental 3d line segment extraction from semi-dense slam[C]//2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018: 1658-1663. PDF, Code