Inter-Image Statistics for Mobile Robot EnvironmentModeling
A Statistical Learning Framework for Inferringthe Geometry of a Scene from Partial Information
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This book presents a statistical learning frameworkfor inferring geometric structures from images.The proposed framework computes dense range maps oflocations in the environment using onlyintensity images and very limited amount of rangedata as an input. This is achieved by integrating andanalyzing the statistical relationships between thevisual data and the available depth on terms of smallpatches. The scientific issue is to represent thiscorrelation such that it can be used to recover rangedata where missing. Markov Random Fields are used asa model to capture the local statistics of theinten...
This book presents a statistical learning frameworkfor inferring geometric structures from images.The proposed framework computes dense range maps oflocations in the environment using onlyintensity images and very limited amount of rangedata as an input. This is achieved by integrating andanalyzing the statistical relationships between thevisual data and the available depth on terms of smallpatches. The scientific issue is to represent thiscorrelation such that it can be used to recover rangedata where missing. Markov Random Fields are used asa model to capture the local statistics of theintensity and range.Experiments on real-world data are conducted underdifferent configurations to demonstrate thefeasibility of the method. In particular, theapplication is in mobile robotics, where inferringthe 3D layout of indoor environments is a criticalproblem for achieving exploration and navigationtasks.Additionally, the proposed method is used inthe color correction and augmentation problem withthe specific application to underwater images.
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