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. In this, accurate estimation of robot orientation is crucial for successful navigation in complex environments; manual labelling of angular values is, though, time-consuming and possibly hard to perform. For this reason, 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 less than 30.00 min.
@article{arxiv.2306.14848,
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},
journal= {arXiv preprint arXiv:2306.14848},
year = {2023}
}