Lightweight and Resource-Efficient Perception for Robotic Guide Dogs
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, wit...
| Authors | Jinse Kwon, Yoojin Lim, Choonghan Lee et al. |
|---|---|
| Primary category | cs.DC |
| Published | 2 October 2026 |
| Last updated at source | 5 October 2026 |
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