Depth from vergence and active calibration for humanoid robots

Authors:
Xin Wang;Boris Lenseigne;Pieter Jonker
Affiliations:
Delft Bio-robotics Lab, Delft University of Technology, Delft, CD, The Netherlands;Delft Bio-robotics Lab, Delft University of Technology, Delft, CD, The Netherlands;Delft Bio-robotics Lab, Delft University of Technology, Delft, CD, The Netherlands
Venue:
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Year:
2012

Citing 10
Cited 0

Real-time vergence control for binocular robots

International Journal of Computer Vision
Analysis of error in depth perception with vergence and spatially varying sensing

Computer Vision and Image Understanding
A Flexible New Technique for Camera Calibration

IEEE Transactions on Pattern Analysis and Machine Intelligence
A compact algorithm for rectification of stereo pairs

Machine Vision and Applications
Introductory Techniques for 3-D Computer Vision

Introductory Techniques for 3-D Computer Vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms

International Journal of Computer Vision
A Binocular Stereo Algorithm for Log-Polar Foveated Systems

BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Peripheral-foveal vision for real-time object recognition and tracking in video

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Cyclorotation models for eyes and cameras

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Embedded foveation image coding

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In human eyes, many clues are used to perceive depth. For nearby tasks involving eye-hand coordination, depth from vergence is a strong cue. In our research on humanoid robots we study binocular robotic eyes that can pan and tilt and perceive depth from stereo, as well as depth from vergence by fixing both eyes on a nearby object. In this paper, we report on a convergent robot vision set-up: Firstly, we describe the mathematical model for convergent vision system. Secondly, we introduce an algorithm to estimate the depth of an object under focus. Thirdly, as the centers of rotation of the eye motors do not align with the center of image planes, we develop an active calibration algorithm to overcome this problem. Finally, we examine the factors that have impact on the depth error. The results of experiments and tests show the good performance of our system and provide insight into depth from vergence.