カメラ・ソナー融合による水中ごみの能動的マッピング
Active Mapping of Underwater Litter Using Camera-Sonar Fusion
前方ソナーとカメラを共有ベイズ占有マップに統合し、不確実性と物体存在確率のバランスで次の視点を選ぶ能動的マッピング手法を提案。シミュレータで従来の網羅パターンより速くごみを発見できることを示した。
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著者: David Rete, Patrick Boros, Lucian Busoniu
分類: cs.RO
原文アブストラクト
Marine litter is a growing threat to the underwater ecosystem, driving demand for autonomous survey methods that can locate debris efficiently over large areas. Existing survey methods typically follow predefined paths or operate with a single sensing modality, typically a camera (with image quality suffering in poor-visibility conditions) or sonar (usually noisy and low-resolution). We present an active mapping framework in which a forward-looking sonar and a camera both feed into a shared Bayesian occupancy map, and an optimization problem is solved at each step to decide on the next best view. Candidate viewpoints are scored by a two-term utility that balances exploration of uncertain regions via voxel entropy against exploitation of likely objects. Each sensor is characterized by range- and bearing-dependent detection and false-alarm probability tables determined from data. We evaluate the approach in a realistic underwater simulator, demonstrating that active mapping finds objects faster than a lawnmower coverage pattern, and that the dual-sensor approach works better than using either of the individual sensors.