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GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion

For more information see https://frobelbest.github.io/gslam

1. Related Papers

  • GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion, C. Tang, O. Wang, P. Tan, In 3DV,2017
  • Global Structure-from-Motion by Similarity Averaging, Z. Cui, P. Tan, In ICCV, 2015

Get two sample sequences from Google Drive .

2. Installation

git clone https://github.com/frobelbest/GSLAM.git

2.1 Required Dependencies

Theia Vision Library (required for global rotation averaging).

Install from http://www.theia-sfm.org

Ceres Solver (required for local and global bundle adjustment).

Install from http://ceres-solver.org

CLP (required for global scale and translation averaging).

Install from https://projects.coin-or.org/Clp

OpenCV (required for image processing).

Install from https://opencv.org

Pangolin (required for visualization).

Install from https://github.com/stevenlovegrove/Pangolin

2.3 Build

Currently, only the xcode project is supplied. You can write your own code to compile on other platforms or wait for future update.

3 Usage

Run on a dataset from Google Drive using GSLAM [sequence_path] [vocabulary_path], for example GSLAM ./robot ./Vocabulary/ORBvoc.txt The ORB vocabulary for loop detection can be downloaded at https://github.com/raulmur/ORB_SLAM2/tree/master/Vocabulary

3.1 Dataset Format.

Under each sequnce folder you will see the following files:

  • shake.mov The input video.
  • framestamp.txt The timestamps for each frame.
  • gyro.txt The gyroscope readings recorded along with the video.
  • config.yaml The config settings.

Notes

Real-time KLT with AVX Acceleration

Except the method proposed in the paper, this project also featured in a highly optimized KLT Tracker that can track more than 4000 points on a 1080p video in real-time.

ToDo

The main bottleneck for this project is the feature tracking, which can be further improved by the paper "Better feature tracking through subspace constraints".

5 License

GSLAM was developed at the Simon Fraser University and Adobe. The open-source version is licensed under the GNU General Public License Version 3 (GPLv3). For commercial purposes, please contact [email protected] or [email protected]