Not an Obstacle for Dog, but a Hazard for Human: A Co-Ego Navigation System for Guide Dog Robots
🤖 AI Summary
This work addresses the challenge of viewpoint asymmetry in guide quadrupedal robots, which, due to their limited ground-level perspective, often fail to detect elevated obstacles—such as overhanging or bent branches—that pose hazards to visually impaired users. To tackle this issue, the authors propose Co-Ego, a novel dual-branch obstacle avoidance framework that fuses the robot’s egocentric ground-level view with the user’s first-person perspective captured via a head-mounted camera. This cross-view perception enables robust detection of high-level obstacles and facilitates collaborative navigation. Experimental results demonstrate that Co-Ego significantly reduces collision rates and cognitive load for blindfolded users compared to single-view or unassisted navigation approaches, thereby enhancing overall navigation safety and reliability in real-world guiding scenarios.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
viewpoint asymmetry
guide dog robots
obstacle avoidance
human-robot navigation
egocentric perspective
Innovation
Methods, ideas, or system contributions that make the work stand out.
viewpoint asymmetry
co-ego navigation
quadruped guide robot
cross-view fusion
obstacle avoidance
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