Abstract: [Purposes]Incorporating building facade 3D data from the ground-view perspective can effectively mitigate geometric distortion and texture blurring from the 3D building models using aerial imagery alone.However,existing studies rarely address automated methods for acquiring ground data.With recent advancements in multi-sensor fusion and simultaneous localization and mapping (SLAM)technologies,unmanned systems have achieved significant improvements in environmental perception and autonomous navigation.[Methods]Based on these new developments,this paper proposes a method for autonomous acquisition of 3D building facade data using an unmanned ground vehicle (UGV).The method automatically generates key acquisition viewpoints from facade geometry information and extracts feasible vehicle pathway from point-cloud data,thereby enabling autonomous path planning and navigation between viewpoints.[Findings]Experimental results demonstrate that:①the point cloud-based passable region classification method for UGV reaches an average accuracy of 92.81%; ②the trajectory error of locally planned paths is less than 0.2 m;③integration of ground data significantly enhances the visual quality and realism of reconstructed 3D models.[Conclusions]Overall,the proposed method exhibits strong accuracy and robustness,providing a highly automated solution for real scene 3D modeling.

Key words: real scene 3D reconstruction, air-ground fusion, SLAM, UGV path planning