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

Last update: Jun 23, 2022

Related tags

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)

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

High performance implementation of Extreme Learning Machines (fast randomized neural networks).

Predict the output which should give a fair idea about the chances of admission for a student for a particular university

Bodywork deploys machine learning projects developed in Python, to Kubernetes.

Retrieve annotated intron sequences and classify them as minor (U12-type) or major (U2-type)

MCML is a toolkit for semi-supervised dimensionality reduction and quantitative analysis of Multi-Class, Multi-Label data

A single Python file with some tools for visualizing machine learning in the terminal.

Practical Time-Series Analysis, published by Packt

Distributed Evolutionary Algorithms in Python

Predicting job salaries from ads - a Kaggle competition

Module for statistical learning, with a particular emphasis on time-dependent modelling

Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

A Powerful Serverless Analysis Toolkit That Takes Trial And Error Out of Machine Learning Projects

Unofficial pytorch implementation of the paper "Context Reasoning Attention Network for Image Super-Resolution (ICCV 2021)"

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

Provide an input CSV and a target field to predict, generate a model + code to run it.

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.

Machine Learning Techniques using python.

A visual dataflow programming language for sklearn

mlpack: a scalable C++ machine learning library --