Geo-registering 3D point clouds to 2D maps with scan matching and the Hough Transform

3D point cloud registration is traditionally done by aligning to known information. This information can be extracted from semantically labeled and geo-registered 2D images, e.g. maps, satellite images, and labeled aerial photos. We propose an automated method to geo-register 3D point clouds to 2D maps by defining a normalized Hough similarity function and aligning planes (i.e., walls) in 3D point clouds to lines in 2D maps. The collective set of algorithms solves for seven degrees of freedom: three rotation parameters (including the up vector), a scale value, and three translation parameters. After transforming the 3D point cloud into a manageable 2D representation, we apply existing and novel scan-matching techniques to align both query and reference representations.