用双视角融合提升导盲机器狗安全性
Not an Obstacle for Dog, but a Hazard for Human: A Co-Ego Navigation System for Guide Dog Robots
- 机器人地面感知+用户高空视角联合避障
- 融合视角后碰撞次数减少，认知负担降低
- 适合关注无障碍导航的AI与机器人研究者
导盲犬能赋予视障人士独立性，但数量有限，多数使用者无法获得。四足导盲机器人是潜在替代方案，但现有系统仅依赖机器人自身的地面传感器导航，忽视了一类关键危险：对机器人透明却在人体高度构成威胁的障碍物，如弯曲的树枝。我们称之为视角不对称问题，并提出首个针对性解决方案——Co-Ego系统。该系统采用双分支避障框架，融合机器人中心的地面感知与用户高处的自我视角，确保导航安全。在四足机器人上部署后，通过三组对照实验（无辅助、单视角、跨视角融合）对蒙眼受试者进行测评。结果表明，跨视角融合显著降低碰撞时间与认知负荷，验证了视角互补对安全导航的必要性。
原文摘要 · Abstract (English)
Guide dogs offer independence to Blind and Low-Vision (BLV) individuals, yet their limited availability leaves the vast majority of BLV users without access. Quadruped robotic guide dogs present a promising alternative, but existing systems rely solely on the robot's ground-level sensors for navigation, overlooking a critical class of hazards: obstacles that are transparent to the robot yet dangerous at human body height, such as bent branches. We term this the viewpoint asymmetry problem and present the first system to explicitly address it. Our Co-Ego system adopts a dual-branch obstacle avoidance framework that integrates the robot-centric ground sensing with the user's elevated egocentric perspective to ensure comprehensive navigation safety. Deployed on a quadruped robot, the system is evaluated in a controlled user study with sighted participants under blindfold across three conditions: unassisted, single-view, and cross-view fusion. Results demonstrate that cross-view fusion significantly reduces collision times and cognitive load, verifying the necessity of viewpoint complementarity for safe robotic guide dog navigation.
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