Stereo Matching as a Nearest-Neighbor Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Rover State Estimation in Challenging Terrain
Autonomous Robots
International Journal of Computer Vision
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
Continuous stereo self-calibration by camera parameter tracking
IEEE Transactions on Image Processing
Factorization of Correspondence and Camera Error for Unconstrained Dense Correspondence Applications
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
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Correlation-based real-time stereo systems have been proven to be effective in applications such as robot navigation, elevation map building etc. This paper provides an in-depth analysis of the major error sources for such a real-time stereo system in the context of cross-country navigation of an autonomous vehicle. Three major types of errors: foreshortening error, misalignment error and systematic error, are identified. The combined disparity errors can easily exceed three-tenths of a pixel, which translates to significant range errors. Upon understanding these error sources, we demonstrate different approaches to either correct them or model their magnitudes without excessive additional computations. By correcting those errors, we show that the precision of the stereo algorithm can be improved by 50%.