ShelfAware: 低コストセンサによる準静的環境でのリアルタイム意味論的ローカリゼーション
ShelfAware: Real-Time Semantic Localization in Quasi-Static Environments with Low-Cost Sensors
棚などの準静的環境で、物体カテゴリの意味情報を粒子フィルタに統合し、低コストな視覚のみのハードウェアでロバストな自己位置推定を実現した研究。
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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: Shivendra Agrawal, Jake Brawer, Ashutosh Naik, Alessandro Roncone, Bradley Hayes
分類: cs.RO, cs.AI
原文アブストラクト
Many indoor workspaces are quasi-static: their global geometric layout is stable, but local semantics change continually, producing repetitive geometry, dynamic clutter, and perceptual noise that defeat standard vision-based localization. We present ShelfAware, a semantic particle filter for robust global localization that treats scene semantics as statistical evidence over object categories rather than fixed quantity landmarks. ShelfAware fuses a depth likelihood with a category-centric semantic similarity and uses a precomputed bank of semantic viewpoints to perform inverse semantic proposals inside Monte Carlo Localization (MCL), yielding fast, targeted hypothesis generation on low-cost, vision-only hardware. To demonstrate perception-agnostic scalability, we evaluate ShelfAware across two domains. In a rigorously controlled mock retail environment, ShelfAware achieves a 97% global localization success rate, maintaining the highest tracking success (66%) across cart, wearable, and dynamic occlusion conditions. Furthermore, in a 3,500 sq. ft. operational grocery store leveraging an open-vocabulary vision pipeline, ShelfAware significantly outperforms both geometric and fixed-quantity semantic baselines. By modeling semantics distributionally and leveraging inverse proposals, ShelfAware resolves geometric aliasing, providing an infrastructure-free building block for mobile and assistive robots in dynamic real-world environments.
