A large scale online UAV mapping algorithm for the dense point cloud and digital surface model generation

doi:10.13474/j.cnki.11-2246.2023.0294

Abstract

Abstract: Aiming at the problem that UAV image acquisition of dense point cloud and DSM is taking too much time and point cloud quality and processing speed are difficult to balance, a large scale online UAV mapping algorithm for the dense point cloud and digital surface model generation is proposed. Firstly, the loose coupling between SLAM and RTK information is used to estimate the image pose online. Then, we use multi-view image pose information in depth space discrete sampling, discrete sampling depth through the current pixel are projected to candidate for getting the matching cost. We use a symmetrical census cost function to compute cost volume, and considering multiple view shade relations joint generation value calculation to save time and improve the accuracy of matching cost. Finally, we propose an incremental cost accumulation strategy to constraint depth range on the current frame using the previous frame depth, to decrease online computation time consuming, and combined with a parabolic curve fitting method to get the higher accuracy and completeness of final depth, the final dense point cloud and DSM results are obtained by projecting the depth map to the object space after drying. The experimental results of three groups of UAV images in typical areas show that our method not only can satisfy the requirements of online computing, but also the accuracy and completeness of point cloud and DSM result are good.

Key words: UAV image, matching cost calculation, incremental cost accumulation, dense point cloud, online computation

CLC Number:

P237

YANG Jiaqi, FAN Dazhao, YANG Jiabin, YANG Xingbin, JI Song. A large scale online UAV mapping algorithm for the dense point cloud and digital surface model generation[J]. Bulletin of Surveying and Mapping, 2023, 0(10): 47-53.

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