toposlam

This repo implements a topological SLAM system. Deep Visual Odometry (DF-VO) and Visual Place Recognition are combined to form the topological SLAM system.

Publications

Visual Odometry Revisited: What Should Be Learnt?

DF-VO: What Should Be Learnt for Visual Odometry?

Scalable Place Recognition Under Appearance Change for Autonomous Driving

@INPROCEEDINGS{zhan2019dfvo,
  author={H. {Zhan} and C. S. {Weerasekera} and J. -W. {Bian} and I. {Reid}},
  booktitle={2020 IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={Visual Odometry Revisited: What Should Be Learnt?}, 
  year={2020},
  volume={},
  number={},
  pages={4203-4210},
  doi={10.1109/ICRA40945.2020.9197374}}

@misc{zhan2021dfvo,
      title={DF-VO: What Should Be Learnt for Visual Odometry?}, 
      author={Huangying Zhan and Chamara Saroj Weerasekera and Jia-Wang Bian and Ravi Garg and Ian Reid},
      year={2021},
      eprint={2103.00933},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{doan2019scalable,
  title={Scalable place recognition under appearance change for autonomous driving},
  author={Doan, Anh-Dzung and Latif, Yasir and Chin, Tat-Jun and Liu, Yu and Do, Thanh-Toan and Reid, Ian},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={9319--9328},
  year={2019}
}

Demo:

Part 1. Requirements

This code was tested with Python 3.6, CUDA 10.0, Ubuntu 16.04, and PyTorch-1.0.

We suggest use Anaconda for installing the prerequisites.

cd envs
conda env create -f min_requirements.yml -p {ANACONDA_DIR/envs/topo_slam} # install prerequisites
conda activate topo_slam  # activate the environment [topo_slam]

Part 2. Download dataset and models

The main dataset used in this project is KITTI Driving Dataset. After downloaing the dataset, create a softlink in the current repo.

ln -s KITTI_ODOMETRY/sequences dataset/kitti_odom/odom_data

For our trained models, please visit here to download the models and save the models into the directory model_zoo/.

Part 3. Run example

# run default kitti setup
python main.py -d options/examples/default.yml  -r data/kitti_odom

More configuration examples can be found in configuration examples.

The result (trajectory pose file) is saved in result_dir defined in the configuration file.
Please check Configuration Documentation for reference.

Part 4. Result evaluation

Please check here for evaluating the result.

License

Please check License file.

GitHub

GitHub - best-of-acrv/toposlam: Topological SLAM: Deep Visual Odometry with Long Term Place Recognition (Loop Closure Detection)
Topological SLAM: Deep Visual Odometry with Long Term Place Recognition (Loop Closure Detection) - GitHub - best-of-acrv/toposlam: Topological SLAM: Deep Visual Odometry with Long Term Place Recogn...
GitHubbest-of-acrv