We are building a cognitive vision system for mobile robots that works in a manner similar to the human vision system, using saccadic, vergence and pursuit movements to extract information from visual input. At each fixation, the system builds a 3D model of a small region, combining information about distance, shape, texture and motion to create a local dynamic spatial model. These local 3D models are composed to create an overall 3D model of the robot and its environment. This approach turns the computer vision problem into a search problem whose goal is the acquisition of sufficient spatial understanding for the robot to succeed at its tasks. The research hypothesis of this work is that the movements of the robot’s cameras are only those that are necessary to build a sufficiently accurate world model for the robot’s current goals. For example, if the goal is to navigate through a room, the model needs to contain any obstacles that would be encountered, giving their approximate positions and sizes. Other information does not need to be rendered into the virtual world, so this approach trades model accuracy for speed.

Date Published: 3 August 2016
PDF: 12 pages
Proc. SPIE 9872, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2016, 98720D (3 August 2016); doi: 10.1117/12.2229551

Published in SPIE Proceedings Vol. 9872:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2016
Jerome J. Braun, Editor(s)