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orb-slam2 with semantic label
| Date | Stars |
|---|---|
| 2026-07-24 | 281 |
| 2026-07-25 | 281 |
| 2026-07-28 | 281 |
| 2026-07-30 | 281 |
| 2026-08-06 | 281 |
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# There are so many large files in .git folder and I hate them, so I move the code to [https://github.com/qixuxiang/orb-slam2_with_semantic_labelling](https://github.com/qixuxiang/orb-slam2_with_semantic_labelling) # orb-slam2_with_semantic_label **Authors:** Xuxiang Qi([email protected]),Shaowu Yang([email protected]),Yuejin Yan([email protected]) **Current version:** 1.0.0 * Note: This repository is mainly built upon [ORB_SLAM2](https://github.com/raulmur/ORB_SLAM2) and [YOLO](https://github.com/pjreddie/darknet/). Many thanks for their great work. ## 0.introduction **orb-slam2_with_semantic_label** is a visual SLAM system based on **[ORB_SLAM2[1-2]](https://github.com/raulmur/ORB_SLAM2)**. The ORB-SLAM2 is a great visual SLAM method that has been popularly applied in robot applications. However, this method cannot provide semantic information in environmental mapping.In this work,we present a method to build a 3D dense semantic map,which utilize both 2D image labels from **[YOLOv3[3]](https://github.com/qixuxiang/YOLOv3_SpringEdition)** and 3D geometric information.  ## 1. Related Publications **[Deep Learning Based Semantic Labelling of 3D Point Cloud in Visual SLAM](https://www.researchgate.net/publication/328005677_Deep_Learning_Based_Semantic_Labelling_of_3D_Point_Cloud_in_Visual_SLAM)** ## 2. Prerequisites ### 2.1 requirements * Ubuntu 14.04/Ubuntu 16.04/Ubuntu 18.04 * ORB-SLAM2 * CUDA * GCC >= 5.0 * cmake * OpenCV * PCL1.7 or PCL1.8, may not work with PCL1.9 * libTorch 1.4 **PS**:(Ubuntu18.04 CUDA10.1 opencv3.4 Eigen3.2.10 PCL1.8 has tested successfully) ### 2.2 Installation Refer to the corresponding original repositories ([ORB_SLAM2](https://github.com/raulmur/ORB_SLAM2) and [YOLO](https://github.com/qixuxiang/YOLOv3_SpringEdition) for installation tutorial). ### 2.3 Build ``` git clone https://github.com/qixuxiang/orb-slam2_with_semantic_label.git sh build.sh ``` ## 3. Run the code 1. Download `yolov3.weights`, `yolov3.cfg` and `coco.names` from [darknet](https://pjreddie.com/darknet/yolo/) and put them in `bin` folder. Also, these files can be found in [YOLO V3](https://github.com/qixuxiang/YOLOv3_SpringEdition).Then, you should make a dir named `img` in `bin` folder, that is, you should execute command `sudo mkdir img` in `bin` folder. you can use [libtorch-yolov3](https://github.com/walktree/libtorch-yolov3) replace libYOLOv3SE, see details https://blog.csdn.net/TM431700/article/details/105889614). 2. Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it to `data` folder. 3. Associate RGB images and depth images using the python script [associate.py](http://vision.in.tum.de/data/datasets/rgbd-dataset/tools). We already provide associations for some of the sequences in `Examples/RGB-D/associations/`. You can generate your own associations file executing: ``` python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt ``` 4. Change `TUMX.yaml` to TUM1.yaml,TUM2.yaml or TUM3.yaml for freiburg1, freiburg2 and freiburg3 sequences respectively. Change `PATH_TO_SEQUENCE_FOLDER`to the uncompressed sequence folder.You can run the project by: ``` cd bin ./rgbd_tum ../Vocabulary/ORBvoc.txt ../Examples/RGB-D/TUM2.yaml ../data/rgbd-data ../data/rgbd-data/associations.txt ```  ## update 1. update 20200705: fix segment fault, make system run faster and use libtorch, thanks for [vayneli](https://github.com/vayneli)! ## Reference [1] Mur-Artal R, Montiel J M M, Tardos J D. ORB-SLAM: a versatile and accurate monocular SLAM system[J]. IEEE Transactions on Robotics, 2015, 31(5): 1147-1163. [2] Mur-Artal R, Tardos J D. ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo a
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0e31de437268b93e, topic:slam, readme:robotics, readme:slam