Paper
6 June 2011 A relaxed fusion of information from real and synthetic images to predict complex behavior
Author Affiliations +
Abstract
An important component of cognitive robotics is the ability to mentally simulate physical processes and to compare the expected results with the information reported by a robot's sensors. In previous work, we have proposed an approach that integrates a 3D game-engine simulation into the robot control architecture. A key part of that architecture is the Match-Mediated Difference (MMD) operation, an approach to fusing sensory data and synthetic predictions at the image level. The MMD operation insists that simulated and predicted scenes are similar in terms of the appearance of the objects in the scene. This is an overly restrictive constraint on the simulation since parts of the predicted scene may not have been previously viewed by the robot. In this paper we propose an extended MMD operation that relaxes the constraint and allows the real and synthetic scenes to differ in some features but not in (selected) other features. Image difference operations that allow a real image and synthetic image generated from an arbitrarily colored graphical model of a scene to be compared. Scenes with the same content show a zero difference. Scenes with varying foreground objects can be controlled to compare the color, size and shape of the foreground.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Damian M. Lyons and D. Paul Benjamin "A relaxed fusion of information from real and synthetic images to predict complex behavior", Proc. SPIE 8064, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2011, 806409 (6 June 2011); https://doi.org/10.1117/12.886014
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Cited by 2 scholarly publications.
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KEYWORDS
Cameras

Image fusion

Visualization

3D modeling

Sensors

Computer simulations

Device simulation

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