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In mobile robotics, the inference of the 3D layout of large-scale indoor environments is a critical problem for achieving exploration and navigation tasks. This article presents a framework for building a 3D model of an indoor environment from partial data using a mobile robot. The modeling of a large-scale environment involves the acquisition of a huge amount of range data to extract the geometry of the scene. This task is physically demanding and time consuming for many real systems. Our approach overcomes this problem by allowing a robot to rapidly collect a set of intensity images and a small amount of range information. The method integrates and analyzes the statistical relationships between the visual data and the limited available depth on terms of small patches and is capable of recovering complete dense range maps. Experiments on real-world data are given to illustrate the suitability of our approach.

Statistics of Visual and Partial Depth Data for Mobile Robot Environment Modeling