外观
论文arXiv 2610.0318726 天前
轻量级且资源高效的机器人导盲犬感知
原文链接：Lightweight and Resource-Efficient Perception for Robotic Guide Dogs（arXiv 2610.03187 · 发布日期：2026-10-02）
Lightweight and Resource-Efficient Perception for Robotic Guide Dogs
面向机器人导盲犬的人本导引，融合360相机与2D LiDAR并结合VLM，在深度验证和GuideDogQA上验证端侧实时感知。
核心要点
- 以往依赖相机或2D/3D LiDAR原始测距，适合避障但缺乏面向用户的语义理解。
- 系统在端侧融合360相机和2D LiDAR，支持避障、运动物体检测与跟踪。
- 遇到无法通行情形时，VLM生成路径解释，作为降低用户焦虑的安全机制。
- 融合深度验证近距可靠但中距有偏差；系统低于55 W实时运行，GuideDogQA达83.8%。
验证范围与局限：验证范围包括融合360相机--LiDAR深度、真实世界第一视角GuideDogQA基准和四足机器人端实时性能。
英文摘要 (Abstract)
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.
作者：Jinse Kwon, Yoojin Lim, Choonghan Lee, Yongseung Yu, Yongin Kwon, Jemin Lee
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