車輪ロボットのビジュアルサーボ：遠隔視点制御におけるロバストな姿勢推定
Visual Servoing on Wheels: Robust Robot Orientation Estimation in Remote Viewpoint Control
ロボットや環境の事前知識を必要とせず、弱教師あり学習で短時間に大量のデータを生成し、遠隔カメラ画像からロボットの向きを高精度に推定するビジュアルサーボのパイプラインを提案。新しい環境への展開を30分以内に可能にした。
著者: Luke Robinson, Daniele De Martini, Matthew Gadd, Paul Newman
分類: cs.RO
原文アブストラクト
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.
