部署运行成的ManhattanSLAM代码压缩包

# ManhattanSLAM **Authors:** Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for **RGB-D** cameras that computes the camera pose trajectory, a sparse 3D reconstruction (containing point, line and plane features) and a dense surfel-based 3D reconstruction. Further details can be found in the related publication. The code is based on [ORB-SLAM2](https://github.com/raulmur/ORB_SLAM2). <a href="https://www.youtube.com/embed/UE8A6mUOPLE" target="_blank"><img src="https://img.youtube.com/vi/UE8A6mUOPLE/0.jpg" alt="ManhattanSLAM" width="240" height="180" border="10" /></a> ### Related Publication: Raza Yunus, Yanyan Li and Federico Tombari, **ManhattanSLAM: Robust Planar Tracking and Mapping Leveraging Mixture of Manhattan Frames**, *in 2021 IEEE International Conference on Robotics and Automation (ICRA) .* **[PDF](https://arxiv.org/pdf/2103.15068.pdf)**. # 1. License ManhattanSLAM is released under a [GPLv3 license](https://github.com/razayunus/ManhattanSLAM/blob/master/License-gpl.txt). For a list of all code/library dependencies (and associated licenses), please see [Dependencies.md](https://github.com/razayunus/ManhattanSLAM/blob/master/Dependencies.md). If you use ManhattanSLAM in an academic work, please cite: ``` @inproceedings{yunus2021manhattanslam, author = {R. Yunus, Y. Li and F. Tombari}, title = {ManhattanSLAM: Robust Planar Tracking and Mapping Leveraging Mixture of Manhattan Frames}, year = {2021}, booktitle = {2021 IEEE international conference on Robotics and automation (ICRA)}, } ``` # 2. Prerequisites We have tested the library in **Ubuntu 16.04** and **Ubuntu 20.04**, but it should be easy to compile on other platforms. A powerful computer (e.g. i7) will ensure real-time performance and provide more stable and accurate results. Following is the list of dependecies for ManhattanSLAM and their versions tested by us: - **OpenCV:** 3.3.0, 3.4.3 - **PCL:** 1.7.2, 1.10 - **Eigen3:** 3.3 - **DBoW2:** Included in Thirdparty folder - **g2o:** Included in Thirdparty folder - **Pangolin** - **tinyply:** 2.3.2 # 3. Building and testing Clone the repository: ``` git clone https://github.com/razayunus/ManhattanSLAM ``` There is a script `build.sh` to build the *Thirdparty* libraries and *ManhattanSLAM*. Please make sure you have installed all required dependencies (see section 2). Execute: ``` cd ManhattanSLAM chmod +x build.sh ./build.sh ``` This will create **libManhattanSLAM.so** in *lib* folder and the executable **manhattan_slam** in *Example* folder. To test the system: 1. Download a sequence for one of the following datasets and uncompress it: - **TUM RGB-D: https://vision.in.tum.de/data/datasets/rgbd-dataset** - **ICL-NUIM: https://www.doc.ic.ac.uk/~ahanda/VaFRIC/iclnuim.html** - **TAMU RGB-D: http://telerobot.cs.tamu.edu/MFG/rgbd/livo/data.html** 2. Associate RGB images and depth images using the python script [associate.py](http://vision.in.tum.de/data/datasets/rgbd-dataset/tools). You can generate an associations file by executing: ``` python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt ``` **Note:** For ICL-NUIM sequences, the association files are already given but the association is defined as ``depth > rgb`` rather than ``rgb > depth``. This can be changed by transforming ``associations.txt`` as: ``` cat associations.txt | sed 's/depth/temp/g;s/rgb/depth/g;s/temp/rgb/g' | tee associations.txt > /dev/null ``` 3. Execute the following command. Change `Config.yaml` to ICL.yaml for ICL-NUIM sequences, TAMU.yaml for TAMU RGB-D sequences or TUM1.yaml, TUM2.yaml or TUM3.yaml for freiburg1, freiburg2 and freiburg3 sequences of TUM RGB-D respectively. Change `PATH_TO_SEQUENCE_FOLDER`to the uncompressed sequence folder. Change `ASSOCIATIONS_FILE` to the path to the corresponding associations file. ``` ./Example/manhattan_slam Vocabulary/ORBvoc.txt Example/Config.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE ```