Abstract: The registration of images and laser point clouds is a key technology for 3D scene reconstruction, providing critical scientific support for origin analysis of explosion accidents and holding broad application prospects in fields such as autonomous driving and disaster origin tracing. However, the spatial scale and geometric feature differences between 2D and 3D data collected by heterogeneous sensors pose challenges to the refined registration of image and point cloud modalities. To this end, this paper proposed a multi-view joint adjustment registration method based on line feature constraints. Firstly, line features were extracted from both images and point cloud data for coarse registration. Then, based on the standard perspective-n-point (PnP) model, constraints of directional consistency and orthogonal consistency of line features were introduced. By aiming to minimize the error function across multiple views, the transformation parameter solution was transformed into a nonlinear least squares problem for iterative optimization, ultimately achieving the accurate registration of images and point clouds. This process does not require 2D-3D projection transformations or scale conversions, thus preventing the introduction of projection errors. Comparative experiments show that the proposed multi-view joint adjustment method with line feature constraints can significantly improve the registration accuracy of image and point cloud modalities, reducing registration errors by over 60% compared to the single-view standard PnP model.

Key words: registration of images and point clouds, multi-view joint adjustment, line feature constraint, PnP model