ロボット盲導犬のための軽量・省リソースな知覚システム
Lightweight and Resource-Efficient Perception for Robotic Guide Dogs
360度カメラと2D LiDARを融合し、歩行者の視点で障害物を認識・説明するオンデバイス知覚モジュールを開発。55W以下でリアルタイム動作し、GuideDogQAベンチマークで83.8%の精度を達成した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Jinse Kwon, Yoojin Lim, Choonghan Lee, Yongseung Yu, Yongin Kwon, Jemin Lee
分類: cs.DC, cs.CV
原文アブストラクト
Robotic guide dogs should understand their surroundings, objects, and potential risks. Prior research has focused on raw sensor data from cameras and 2D or 3D LiDAR, which precisely measure distance points rather than provide a semantic understanding of the scene. While these physical measurements are effective for robot-centric collision avoidance and robot safety, they are not suitable for human-centric guidance. The system should recognize the type and relevance of obstacles and explain them, clearly and actionably, in terms of their spatial relation to the user. We present complete on-device perception modules that fuse a 360 camera and a 2D LiDAR for reliable collision avoidance, with moving-object detection and tracking for human-centric guidance. Finally, in walking-impossible situations, a vision--language model delivers pathway explanations as a safety mechanism to reduce user anxiety. In experiments, verification of fused 360 camera--LiDAR depth shows reliable near-range perception but inherent mid-range bias, while the system as a whole sustained real-time performance under 55 W. On the real-world egocentric GuideDogQA benchmark, our system achieved 83.8\% accuracy, compared with 67.1\% for GPT-4o. These results demonstrate that practical human-centric guidance with real-time on-device inference is feasible even on quadrupeds.