Author: Maxime Vaidis maxime.vaidis@norlab.ulaval.ca
Update: September 30th 2023 by Maxime Vaidis
Code to process RTS data
This RTS library aims to develop the processing of Robotic Total Stations data in mobile robotics. Codes, datasets and examples are provided in this repository. This library is linked to the project RTS of the Norlab Laboratory.
Contents:
RTS_Extrinsic_Calibration is supported on Python 3.8.14 on Ubuntu 22.04.
You might also want to use a virtual environment for the installation.
The requirements.txt file contains all the packages needed for the installation.
Details about the code are in the Wiki.
A 2023 dataset consisting of .bag files with RTS data is available Here with a details explanation about the data collected.
The RTS-GT dataset was taken with two different robotic platform and contains over 49 kilometers of prism trajectories tracked by three RTSs.
Note: the dataset of 2023 was collected through ROS 1 for the RTS data, and ROS 2 for the robots' data
A few "inoffical" scripts for special use-cases are collected in the contrib/ directory of the repository.
They are inofficial in the sense that they don't ship with the package distribution and thus aren't regularly tested in continuous integration.
Patches are welcome, preferably as pull requests.
First aid:
- Check the Wiki
- Check the previous issues
- Open a new issue
If you use this package for your research, a footnote with the link to this repository is appreciated: github.com/norlab-ulaval/RTS_Extrinsic_Calibration.
2023: RTS-GT: Robotic Total Stations Ground Truthing dataset (Submitted to ICRA 2024)
2023: Extrinsic calibration for highly accurate trajectories reconstruction (ICRA 2023)
2021: Accurate outdoor ground truth based on total stations (CRV 2021)