GSAT: 自己教師あり学習と異常検知による多様な地形の幾何学的走行可能性推定
GSAT: Geometric Traversability Estimation using Self-supervised Learning with Anomaly Detection for Diverse Terrains
ロボットの安全な自律航法のため、自己教師あり学習と異常検知を組み合わせ、負例なしで走行可能領域を分類する新しい手法を提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Dongjin Cho, Miryeong Park, Juhui Lee, Geonmo Yang, Younggun Cho
分類: cs.RO
原文アブストラクト
Safe autonomous navigation requires reliable estimation of environmental traversability. Traditional methods have relied on semantic or geometry-based approaches with human-defined thresholds, but these methods often yield unreliable predictions due to the inherent subjectivity of human supervision. While self-supervised approaches enable robots to learn from their own experience, they still face a fundamental challenge: the positive-only learning problem. To address these limitations, recent studies have employed Positive-Unlabeled (PU) learning, where the core challenge is identifying positive samples without explicit negative supervision. In this work, we propose GSAT, which addresses these limitations by constructing a positive hypersphere in latent space to classify traversable regions through anomaly detection without requiring additional prototypes (e.g., unlabeled or negative). Furthermore, our approach employs joint learning of anomaly classification and traversability prediction to more efficiently utilize robot experience. We comprehensively evaluate the proposed framework through ablation studies, validation on heterogeneous real-world robotic platforms, and autonomous navigation demonstrations in simulation environments.
