Visual Servoing on Wheels: Robust Robot Orientation Estimation in Remote Viewpoint Control
Luke Robinson, Daniele De Martini, Matthew Gadd, Paul Newman
Abstract
This work proposes a fast deployment pipeline for visually-servoed robots which does not assume anything about either the robot - e.g. sizes, colour or the presence of markers - or the deployment environment. Specifically, we apply a learning based approach to reliably estimate the pose of a robot in the image frame of a 2D camera upon which a visual servoing control system can be deployed. To alleviate the time-consuming process of labelling image data, we propose a weakly supervised pipeline that can produce a vast amount of data in a small amount of time. We evaluate our approach on a dataset of remote camera images captured in various indoor environments demonstrating high tracking performances when integrated into a fully-autonomous pipeline with a simple controller. With this, we then analyse the data requirement of our approach, showing how it is possible to deploy a new robot in a new environment in fewer than 30.00 min.
BibTeX
@inproceedings{iros2023_visualservoingon,
title = {Visual Servoing on Wheels: Robust Robot Orientation Estimation in Remote Viewpoint Control},
author = {Luke Robinson and Daniele De Martini and Matthew Gadd and Paul Newman},
booktitle = {IROS 2023},
year = {2023}
}