RVWO: A Robust Visual-Wheel SLAM System for Mobile Robots in Dynamic Environments
Jaafar Mahmoud, Andrey Penkovskiy, Ha The Long Vuong, Aleksey Burkov, Sergey Kolyubin
Abstract
This paper presents RVWO, a system designed to provide robust localization and mapping for wheeled mobile robots in challenging scenarios. The proposed approach leverages a probabilistic framework that incorporates semantic prior information about landmarks and visual re-projection error to create a landmark reliability model, which acts as an adaptive kernel for the visual residuals in optimization. Additionally, we fuse visual residuals with wheel odometry measurements, taking advantage of the planar motion assumption. The RVWO system is designed to be robust against wrong data association due to moving objects, poor visual texture, bad illumination, and wheel slippage. Evaluation results demonstrate that the proposed system shows competitive results in dynamic environments and outperforms existing approaches on both public benchmarks and our custom hardware setup. We also provide the code as an open-source contribution to the robotics community22https://github.com/be2rlab/rvwo.
BibTeX
@inproceedings{iros2023_rvwoarobustvisua,
title = {RVWO: A Robust Visual-Wheel SLAM System for Mobile Robots in Dynamic Environments},
author = {Jaafar Mahmoud and Andrey Penkovskiy and Ha The Long Vuong and Aleksey Burkov and Sergey Kolyubin},
booktitle = {IROS 2023},
year = {2023}
}