Range synthesis for 3D environment modeling

This paper examines a novel method we have developed for computing range data in the context of mobile robotics. Our objective is to compute dense range maps of locations in the environment, but to do this using intensity images and very limited range data as input. We develop a statistical learning method for inferring and extrapolating range data from a combination of a single video intensity image and a limited amount of input range data. Our methodology is to compute the relationship between the observed range data and the variations in the intensity image, and use this to extrapolate new range values. These variations can be efficiently captured by the neighborhood system of a Markov random field (MRF) without making any strong assumptions about the kind of surfaces in the world. Experimental results show the feasibility of our method.

Authors

• Luz Abril Torres-Méndez
• Gregory Dudek

Keywords

• Mobile robots
• Intelligent robots
• Markov random fields
• Navigation
• Markov processes
• Pixel
• Machine intelligence
• Mobile computing
• Statistical learning
• Pervasive computing
• Image Intensity
• Strong Assumptions
• Mobile Robot
• Markov Random Field
• Limited Amount Of Data
• Range Maps
• Neighborhood System
• Volume Of Data
• Average Error
• Typical Experiment
• Residual Error
• Local Properties
• Case Example
• Surface Reflectance
• Intensity Information
• Depth Values
• Mobile Platform
• Neighborhood Size
• Unknown Data
• Laser Ranging