To address the issue of improving accuracy and efficiency in multi-UAV platforms for collaborative environmental sensing tasks,a study on visual dense mapping and map fusion technologies for multi-UAV was conducted.Firstly,to tackle the problems of invalid map points and local ghosting caused by mapping errors and sensor inaccuracies,a visual dense point cloud mapping algorithm was proposed.This algorithm constructs dense point cloud maps by filtering the point clouds and employing a combination of generalized nearest neighbor iteration based on KD-Tree and voxel filtering.Subsequently,a dense map fusion algorithm based on overlapping regions was introduced.This algorithm determines the fusion of growing sub-maps by obtaining overlapping regions and identifying correct matching points,thereby achieving dense map fusion.Finally,a dual-UAV validation platform was built.Outdoor scene experiments demonstrated that the proposed method can maintain mapping errors within 5%,effectively reduce the number of map points,and achieve precise map fusion.
维普
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