Real-time dense reconstruction using Unmanned Aerial Vehicle (UAV) is becoming increasingly popular in large-scale rescue and environmental monitoring tasks. However, due to the energy constraints of a single UAV, the efficiency can be greatly improved through the collaboration of multi-UAVs. Nevertheless, when faced with unknown environments or the loss of Global Navigation Satellite System (GNSS) signal, most multi-UAV SLAM systems can’t work, making it hard to construct a global consistent map. In this paper, we propose a real-time dense reconstruction system called AutoFusion for multiple UAVs, which robustly supports scenarios with lost global positioning and weak co-visibility. A method for Visual Geolocation and Matching Network (VGMN) is suggested by constructing a graph convolutional neural network as a feature extractor. It can acquire geographical location information solely through images. We also present a real-time dense reconstruction framework for multi-UAV with autonomous visual geolocation. UAV agents send images and relative positions to the ground server, which processes the data using VGMN for multi-agent geolocation optimization, including initialization, pose graph optimization, and map fusion. Extensive experiments demonstrate that our system can efficiently and stably construct large-scale dense maps in real-time with high accuracy and robustness.