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.

翻译：导盲犬为盲人和低视力（BLV）个体提供了独立性，但其有限的可及性使得绝大多数BLV用户无法获得帮助。四足机器人导盲犬是一种有前景的替代方案，但现有系统仅依赖机器人的地面级传感器进行导航，忽视了一类关键危险：那些对机器人而言透明但可能在人体高度造成危险的障碍物，例如弯曲的树枝。我们称之为视角不对称问题，并首次提出明确解决该问题的系统。我们的共自我系统采用双分支避障框架，将以机器人为中心的地面感知与用户的高处自我视角相结合，以确保全面的导航安全性。该系统部署在四足机器人上，在受控的用户研究中进行了评估，参与者为蒙眼视觉正常的参与者，共三种条件：无辅助、单视角和跨视角融合。结果表明，跨视角融合显著减少了碰撞次数和认知负荷，验证了视角互补性对于安全的机器人导盲犬导航的必要性。