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This work proposes a fast deployment pipeline for visually-servoed robotswhich does not assume anything about either the robot - e.g. sizes, colour orthe presence of markers - or the deployment environment. In this, accurateestimation of robot orientation is crucial for successful navigation in complexenvironments; manual labelling of angular values is, though, time-consuming andpossibly hard to perform. For this reason, we propose a weakly supervisedpipeline that can produce a vast amount of data in a small amount of time. Weevaluate our approach on a dataset of remote camera images captured in variousindoor environments demonstrating high tracking performances when integratedinto a fully-autonomous pipeline with a simple controller. With this, we thenanalyse the data requirement of our approach, showing how it is possible todeploy a new robot in a new environment in less than 30.00 min.

Visual Servoing on Wheels: Robust Robot Orientation Estimation in Remote Viewpoint Control