This work presents a novel approach to registering multiple range images on top of a Google Maps image. The fundamental concept behind the method is matching completely different types of input with each other using classification as a middleman. Range images and Google Maps images are separated into classes, and the range image is also projected into a 2D top-down template image. The template image can then be matched against the Google Maps image to find its location and orientation on the map, which can be used for registering the range images. An experiment comparing this technique against using GPS to find position and orientation showed that it is effective at automatically constructing a reasonable large-scale 3D model whereas GPS would be completely ineffective.

This paper presents a method for accurately segmenting and classifying 3D range data into particular object classes. Object classification of input images is necessary for applications including robot navigation and automation, in particular with respect to path planning. To achieve robust object classification, we propose the idea of an object feature which represents a distribution of neighboring points around a target point. In addition, rather than processing raw points, we reconstruct polygons from the point data, introducing connectivity to the points. With these ideas, we can refine the Markov Random Field (MRF) calculation with more relevant information with regards to determining ldquorelated pointsrdquo. The algorithm was tested against five outdoor scenes and provided accurate classification even in the presence of many classes of interest.