A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

Overview

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.

ManhattanSLAM

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.

1. License

ManhattanSLAM is released under a GPLv3 license. For a list of all code/library dependencies (and associated licenses), please see 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, 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
  • PCL: 1.7.2
  • Eigen3: 3.3
  • DBoW2: Included in Thirdparty folder
  • g2o: Included in Thirdparty folder
  • Pangolin
  • tinyply

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:

  2. Associate RGB images and depth images using the python script associate.py. You can generate an associations file by executing:

python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt
  1. 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_FOLDERto 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
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