Not an Obstacle for Dog, but a Hazard for Human: A Co-Ego Navigation System for Guide Dog Robots

TL;DR

This paper introduces a co-ego navigation system for guide dog robots that combines ground-level and user-elevated perspectives to improve obstacle avoidance, especially for hazards invisible to the robot but dangerous to humans.

Contribution

It presents the first dual-branch obstacle avoidance framework that explicitly addresses viewpoint asymmetry in robotic guide dogs, enhancing safety through perspective fusion.

Findings

Cross-view fusion reduces collision times.

It lowers cognitive load during navigation.

The system improves safety in obstacle avoidance.

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.