When firefighting robots are deployed for medium to long-distance emergency tasks in urban areas, they often struggle with the inability to obtain a global prior map of the environment in advance. Consequently, they require manual remote control to reach the fire location, which involves cumbersome operations and significantly reduces firefighting efficiency. To address these issues, this study designs a new autonomous navigation system for firefighting robots in urban areas. This system is based on commercial electronic maps (such as Amap, Baidu Maps, and other 2D electronic maps) and effectively integrates the global navigation satellite system (GNSS) with local laser-based environmental sensing technologies. Firstly, commercial electronic maps are used to plan rough global sub-goal points. The sequence of global goal points is then registered with the actual positioning information and sent to the local planner. Subsequently, local planning tasks are performed within the local grid map established by laser sensing, following the sequence of sub-goal points. The improved local planner updates the sub-goal points dynamically based on real-time environmental changes during movement. Multiple simulations are conducted in a simulated environment, and validation is performed using a tracked vehicle in real-world scenarios. The results indicate that the designed system can accurately execute long-distance outdoor navigation tasks without a global prior map of the environment, providing an efficient and safe solution for the outdoor navigation of firefighting robots.