A robust measurable modeling method fusing three-dimensional reconstruction and geographic registration

Abstract

The application discloses a robust measurable modeling method fusing three-dimensional reconstruction and geographic registration, and belongs to the technical field of three-dimensional modeling. The method comprises the following steps: acquiring source point cloud data generated by three-dimensional reconstruction and target point cloud data with geographic coordinate reference; completing initial registration based on an Umeyama algorithm; iteratively optimizing initial transformation parameters by using a Meishan-iterative reweighted least square-Huber weight algorithm, and realizing geographic registration through robust standard deviation estimation, Huber weight distribution and convergence judgment, so as to generate a measurable geographic space three-dimensional model; and based on the model, volume calculation, distance measurement and space analysis can be carried out. The application can inhibit noise and abnormal point interference through a robust iterative optimization mechanism, has high registration accuracy, and the generated model has unified geographic coordinate reference and high-precision measurement function, thereby solving the problems of poor noise resistance and lack of geographic reference of traditional three-dimensional models, and can be widely applied to engineering surveying and mapping, space planning and other scenes.

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