教師なしオンライン学習によるロボットの興味度推定のための視覚的記憶性
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning
移動ロボットが興味深いシーンを予測するため、並進不変な視覚記憶と長期・短期・オンライン学習の3段階アーキテクチャを提案し、既存手法より高い精度を達成した。
著者: Chen Wang, Wenshan Wang, Yuheng Qiu, Yafei Hu, Sebastian Scherer
分類: cs.CV, cs.RO
原文アブストラクト
In this paper, we explore the problem of interesting scene prediction for mobile robots. This area is currently underexplored but is crucial for many practical applications such as autonomous exploration and decision making. Inspired by industrial demands, we first propose a novel translation-invariant visual memory for recalling and identifying interesting scenes, then design a three-stage architecture of long-term, short-term, and online learning. This enables our system to learn human-like experience, environmental knowledge, and online adaption, respectively. Our approach achieves much higher accuracy than the state-of-the-art algorithms on challenging robotic interestingness datasets.
