SignNav: 大規模屋内環境における標識を活用した意味的視覚ナビゲーション
SignNav: Leveraging Signage for Semantic Visual Navigation in Large-Scale Indoor Environments
標識の意味的手がかりを解釈して目的地へ移動する新しいナビゲーションタスクSignNavを提案し、空間・時間認識トランスフォーマー(START)モデルで80%の成功率を達成した。
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著者: Jian Sun, Yuming Huang, He Li, Shuqi Xiao, Shenyan Guo, Maani Ghaffari, Qingbiao Li, Chengzhong Xu, Hui Kong
分類: cs.RO, cs.CV
原文アブストラクト
Humans routinely leverage semantic hints provided by signage to navigate to destinations within novel Large-Scale Indoor (LSI) environments, such as hospitals and airport terminals. However, this capability remains underexplored within the field of embodied navigation. This paper introduces a novel embodied navigation task, SignNav, which requires the agent to interpret semantic hint from signage and reason about the subsequent action based on current observation. To facilitate research in this domain, we construct the LSI-Dataset for the training and evaluation of various SignNav agents. Dynamically changing semantic hints and sparse placement of signage in LSI environments present significant challenges to the SignNav task. To address these challenges, we propose the Spatial-Temporal Aware Transformer (START) model for end-to-end decision-making. The spatial-aware module grounds the semantic hint of signage into physical world, while the temporal-aware module captures long-range dependencies between historical states and current observation. Leveraging a two-stage training strategy with Dataset Aggregation (DAgger), our approach achieves state-of-the-art performance, recording an 80% Success Rate (SR) and 0.74 NDTW on val-unseen split. Real-world deployment further demonstrates the practicality of our method in physical environment without pre-built map.
