Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios

Abstract

This paper presents a 3D vision-based autonomous navigation system for wheeled mobile robots equipped with an RGB-D camera. The system integrates SLAM (simultaneous localization and mapping), motion planning, and obstacle avoidance to operate in both static and dynamic environments. A real-time pipeline is developed to construct sparse and dense maps for precise localization and path planning. Navigation meshes (NavMeshes) derived from 3D reconstructions facilitate efficient A* path planning. Additionally, a dynamic “U-map” generated from depth data identifies obstacles, enabling rapid NavMesh updates for obstacle avoidance. The proposed system achieves real-time performance and robust navigation across diverse terrains, including uneven surfaces and ramps, offering a comprehensive solution for 3D vision-guided robotic navigation.