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

Related tags

Machine Learningtoposlam
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

  1. Visual Odometry Revisited: What Should Be Learnt?

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

  3. 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:

Contents

  1. Requirements
  2. Prepare dataset
  3. Run example
  4. Result evaluation

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.

Owner
Best of Australian Centre for Robotic Vision (ACRV)
A collection of open source projects capturing the best of the ACRV. See link below to further explore our projects.
Best of Australian Centre for Robotic Vision (ACRV)
GitHub Repository
To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction

To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction. The challenge aims to adress the problems of medical imbalanced data classification.

Marwan Mashra 1 Jan 31, 2022
Basic Docker Compose for Machine Learning Purposes

Docker-compose for Machine Learning How to use: cd docker-ml-jupyterlab

Chris Chen 1 Oct 29, 2021
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Intel(R) Extension for Scikit-learn* Installation | Documentation | Examples | Support | FAQ With Intel(R) Extension for Scikit-learn you can accelera

Intel Corporation 858 Dec 25, 2022
Diabetes Prediction with Logistic Regression

Diabetes Prediction with Logistic Regression Exploratory Data Analysis Data Preprocessing Model & Prediction Model Evaluation Model Validation: Holdou

AZİZE SULTAN PALALI 2 Oct 23, 2021
Transform ML models into a native code with zero dependencies

m2cgen (Model 2 Code Generator) - is a lightweight library which provides an easy way to transpile trained statistical models into a native code

Bayes' Witnesses 2.3k Jan 03, 2023
Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.

Call of Duty World League: Search & Destroy Outcome Predictions Growing up as an avid Call of Duty player, I was always curious about what factors led

Brett Vogelsang 2 Jan 18, 2022
Code Repository for Machine Learning with PyTorch and Scikit-Learn

Code Repository for Machine Learning with PyTorch and Scikit-Learn

Sebastian Raschka 1.4k Jan 03, 2023
Repositório para o #alurachallengedatascience1

1° Challenge de Dados - Alura A Alura Voz é uma empresa de telecomunicação que nos contratou para atuar como cientistas de dados na equipe de vendas.

Sthe Monica 16 Nov 10, 2022
Greykite: A flexible, intuitive and fast forecasting library

The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.

LinkedIn 1.7k Jan 04, 2023
A machine learning model for Covid case prediction

CovidcasePrediction A machine learning model for Covid case prediction Problem Statement Using regression algorithms we can able to track the active c

VijayAadhithya2019rit 1 Feb 02, 2022
Hierarchical Time Series Forecasting using Prophet

htsprophet Hierarchical Time Series Forecasting using Prophet Credit to Rob J. Hyndman and research partners as much of the code was developed with th

Collin Rooney 131 Dec 02, 2022
This is my implementation on the K-nearest neighbors algorithm from scratch using Python

K Nearest Neighbors (KNN) algorithm In this Machine Learning world, there are various algorithms designed for classification problems such as Logistic

sonny1902 1 Jan 08, 2022
Cryptocurrency price prediction and exceptions in python

Cryptocurrency price prediction and exceptions in python This is a coursework on foundations of computing module Through this coursework i worked on m

Panagiotis Sotirellos 1 Nov 07, 2021
Painless Machine Learning for python based on scikit-learn

PlainML Painless Machine Learning Library for python based on scikit-learn. Install pip install plainml Example from plainml import KnnModel, load_ir

1 Aug 06, 2022
machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure cloud using Azure app service

This is a machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure cloud using Azure app service. We initially made th

Krishna Priyatham Potluri 73 Dec 01, 2022
CS 7301: Spring 2021 Course on Advanced Topics in Optimization in Machine Learning

CS 7301: Spring 2021 Course on Advanced Topics in Optimization in Machine Learning

Rishabh Iyer 141 Nov 10, 2022
This machine learning model was developed for House Prices

This machine learning model was developed for House Prices - Advanced Regression Techniques competition in Kaggle by using several machine learning models such as Random Forest, XGBoost and LightGBM.

serhat_derya 1 Mar 02, 2022
It is a forest of random projection trees

rpforest rpforest is a Python library for approximate nearest neighbours search: finding points in a high-dimensional space that are close to a given

Lyst 211 Dec 29, 2022
Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice

Responsible AI Workshop Responsible innovation is top of mind. As such, the tech industry as well as a growing number of organizations of all kinds in

Microsoft 9 Sep 14, 2022
This is a public repo where code samples are stored for the book Practical MLOps.

[Book-2021] Practical MLOps O'Reilly Book

Pragmatic AI Labs 421 Dec 31, 2022