Mobile Robot Navigation Method Based on Multiple External Cameras in Crowded Environment

Existing navigation approaches for mobile robots in crowded environments predominantly rely on on-board sensors like LiDAR and monocular cameras, suffering from limited sensing coverage and occlusion issues that hinder comprehensive perception of dynamic surroundings. This paper presents a novel navigation framework leveraging a multi-camera system deployed in the environment to enable holistic environmental perception and robust robot navigation. The framework introduces a Generalized Multi-View Detection (GMVD) algorithm with learnable adaptive projection and dynamic view fusion, which uses markers to assist in robot localization. The navigation layer integrates an improved A* algorithm with a hierarchical strategy combining speed barriers and dynamic window approaches to achieve collision-free path planning. Real-world experiments comparing the proposed method with previous crowd navigation algorithms demonstrate that it significantly enhances the robot's navigation performance, generating obstacle-free paths for safe and efficient navigation in crowded scenarios.