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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.",[11,12,13,14,15],"Xiao, Ruofu","Xie, Da","Jia, Fuhua","Ijaz, Salman","Rushworth, Adam",[17,18,19,19,20],"0000-0003-4629-2873","0000-0003-2744-3349",null,"0000-0003-3803-7549",2025,"https:\u002F\u002Fdoi.org\u002F10.1109\u002Fivcnz67716.2025.11281860","W4417249175",[25,26,27,28,29,30,31],"Mobile robot navigation","Mobile robot","Robot","Navigation system","Path (computing)","Window (computing)","Motion 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