どこを見るべきかを学習：幾何情報を用いた能動的定位のための自己教師あり視点選択
Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information
幾何情報を活用した自己教師あり学習により、ロボットが定位精度を高めるための視点をリアルタイムに選択する手法を提案し、合成・実データで既存手法を上回る性能を示した。
著者: Luca Di Giammarino, Boyang Sun, Giorgio Grisetti, Marc Pollefeys, Hermann Blum, Daniel Barath
分類: cs.CV, cs.RO
原文アブストラクト
Accurate localization in diverse environments is a fundamental challenge in computer vision and robotics. The task involves determining a sensor's precise position and orientation, typically a camera, within a given space. Traditional localization methods often rely on passive sensing, which may struggle in scenarios with limited features or dynamic environments. In response, this paper explores the domain of active localization, emphasizing the importance of viewpoint selection to enhance localization accuracy. Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications. Our results demonstrate that our method performs better than the existing one, targeting similar problems and generalizing on synthetic and real data. We also release an open-source implementation to benefit the community.
