This repo implements a Topological SLAM: Deep Visual Odometry with Long Term Place Recognition (Loop Closure Detection)

Last update: Jun 23, 2022

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Overview

Introduction

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

@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}
}

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.

Acknowledgement

Some of the codes were borrowed from the excellent works of monodepth2, LiteFlowNet and pytorch-liteflownet. The borrowed files are licensed under their original license respectively.

This repo implements a Topological SLAM: Deep Visual Odometry with Long Term Place Recognition (Loop Closure Detection)

Related tags

Overview

Introduction

Publications

Demo:

Contents

Part 1. Requirements

Part 2. Download dataset and models

Part 3. Run example

Part 4. Result evaluation

License

Acknowledgement

Owner

Best of Australian Centre for Robotic Vision (ACRV)

This is a public repo where code samples are stored for the book Practical MLOps.

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This repo implements a Topological SLAM: Deep Visual Odometry with Long Term Place Recognition (Loop Closure Detection)

Related tags

Overview

Introduction

Publications

Demo:

Contents

Part 1. Requirements

Part 2. Download dataset and models

Part 3. Run example

Part 4. Result evaluation

License

Acknowledgement

Owner

Best of Australian Centre for Robotic Vision (ACRV)

This is a public repo where code samples are stored for the book Practical MLOps.

A Time Series Library for Apache Spark

Required for a machine learning pipeline data preprocessing and variable engineering script needs to be prepared

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

A machine learning project that predicts the price of used cars in the UK

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

﻿Greykite: A flexible, intuitive and fast forecasting library

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

XGBoost + Optuna

This is a Machine Learning model which predicts the presence of Diabetes in Patients

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LibRerank is a toolkit for re-ranking algorithms. There are a number of re-ranking algorithms, such as PRM, DLCM, GSF, miDNN, SetRank, EGRerank, Seq2Slate.

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