2D Mapping Considering Potential Occupancy Space of Mobile Objects for a Guide Dog Robot

This research proposes a 2D mapping method that considers the potential occupancy space of mobile objects for a guide dog robot, aiming at enhancing safe navigation for visually impaired individuals. By using a RS-LiDAR-16 sensor and an RGB-D camera, the guide dog robot realized robust 2D mapping by integrating cartographer based 2D mapping, object recognition results by using YOLACT, and Harris Corner detection. Meanwhile, a novel dynamic risk map, responsive to the robot’s position, is developed to avoid collisions with suddenly obstacles appearing from blink spots like doors and corners. Experimental results demonstrate that the generated dynamic risk map significantly improves the performance of collision avoidance, reduces moving time, and increases the flexibility of the guidance route, proving the effectiveness of the proposed method in dynamic environments.