マルチエージェント協調のための能動的視覚自己位置推定：データ駆動型アプローチ
Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach
異なる視点を持つロボットやデバイスの地図上で自己位置を推定するため、最適な視点を選択するデータ駆動型手法を提案し、シミュレーションと実環境で既存手法を上回る性能を示した。
著者: Matthew Hanlon, Boyang Sun, Marc Pollefeys, Hermann Blum
分類: cs.RO, cs.CV
原文アブストラクト
Rather than having each newly deployed robot create its own map of its surroundings, the growing availability of SLAM-enabled devices provides the option of simply localizing in a map of another robot or device. In cases such as multi-robot or human-robot collaboration, localizing all agents in the same map is even necessary. However, localizing e.g. a ground robot in the map of a drone or head-mounted MR headset presents unique challenges due to viewpoint changes. This work investigates how active visual localization can be used to overcome such challenges of viewpoint changes. Specifically, we focus on the problem of selecting the optimal viewpoint at a given location. We compare existing approaches in the literature with additional proposed baselines and propose a novel data-driven approach. The result demonstrates the superior performance of the data-driven approach when compared to existing methods, both in controlled simulation experiments and real-world deployment.
