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https://github.com/utiasASRL/vtr3

VT&R3 is a C++ implementation of the Teach and Repeat navigation framework. It enables a robot to be taught a network of traversable paths and then closely repeat any part of the network.
https://github.com/utiasASRL/vtr3

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VT&R3 is a C++ implementation of the Teach and Repeat navigation framework. It enables a robot to be taught a network of traversable paths and then closely repeat any part of the network.

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README 

        
# Visual Teach & Repeat 3 (VT&R3)



## What is VT&R3?



VT&R3 is a C++ implementation of the Teach and Repeat navigation framework. It enables a robot to be taught a network of traversable paths and then closely repeat any part of the network. VT&R3 is designed for easy adaptation to various sensors (camera, lidar, radar, GPS, etc) and robot combinations. So far, we have explored using VT&R3 to perform teach and repeat navigation using a stereo camera, a lidar, or a combination of a stereo camera and GPS.



## Software Support

This repository contains active support for the following feature

* Multi-experience localization with a Stereo Camera

* Deep-learned visual features with a Stereo Camera

* LiDAR ICP odometry and localization



With support for so many sensors, the repository has grown quite large. 

To reduce the required compilation time an environment variable `VTR_PIPELINE` has been added to allow for the pipeline to be selected at compile time, instead of run time. 

The supported pipelines are:

* `LIDAR`

* `VISION`

* `RADAR`

* `RADAR_LIDAR`

  

If the variable is unset, then all pipelines will be compiled and the user can select at run time through the config file parameter `pipeline.type` which pipeline to use.



The primary support version of VTR requires an NVidia Driver with Cuda capabilities.

The current Dockerfile requires a CUDA driver capable of supporting 11.7.

A GPU is required for all versions of the vision (camera) pipeline and features for LiDAR and RADAR that use PyTorch models for processing.



If no GPU is available a CPU only version is available, but **only for LiDAR**.

Note that the CPU version of TorchLib is installed for easier compilation but the models are unlikely to run fast enough on a CPU to be useful. 



## Reproducing Results of VT&R3 Papers



VT&R3 related papers usually focus on demonstrating one specific feature of VT&R3 instead of the whole system and require additional scripts to run experiments and evaluate results. Therefore, from now on, we will create a separate repository for each paper with instructions on how to reproduce the results.



- [Burnett et al., Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization?, IROS'22](https://github.com/utiasASRL/radar_topometric_localization)



## Knowing the Codebase



The following articles will help you get familiar with VT&R3:



- [Installation](https://github.com/utiasASRL/vtr3/wiki/Installation)

- [Getting Started](https://github.com/utiasASRL/vtr3/wiki/Getting-Started)

- [Demo Datasets](https://github.com/utiasASRL/vtr3/wiki/VTR3-Sample-Datasets)



More information can be found on the [wiki page](https://github.com/utiasASRL/vtr3/wiki).



## Citation



Please cite the following [paper](https://onlinelibrary.wiley.com/doi/full/10.1002/rob.20342) when using VT&R3 for your research:



```bibtex

@article{paul2010vtr,

  author = {Furgale, Paul and Barfoot, Timothy D.},

  title = {Visual teach and repeat for long-range rover autonomy},

  journal = {Journal of Field Robotics},

  year = {2010},

  doi = {https://doi.org/10.1002/rob.20342}

}

```



## [License](./LICENSE)