Error analysis in stereo vision for location measurement of 3D point

Yunting Li; Jun Zhang; Jinwen Tian

doi:10.1117/12.2203640

14 December 2015 Error analysis in stereo vision for location measurement of 3D point

Yunting Li, Jun Zhang, Jinwen Tian

Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 981315 (2015) https://doi.org/10.1117/12.2203640
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

Location measurement of 3D point in stereo vision is subjected to different sources of uncertainty that propagate to the final result. For current methods of error analysis, most of them are based on ideal intersection model to calculate the uncertainty region of point location via intersecting two fields of view of pixel that may produce loose bounds. Besides, only a few of sources of error such as pixel error or camera position are taken into account in the process of analysis. In this paper we present a straightforward and available method to estimate the location error that is taken most of source of error into account. We summed up and simplified all the input errors to five parameters by rotation transformation. Then we use the fast algorithm of midpoint method to deduce the mathematical relationships between target point and the parameters. Thus, the expectations and covariance matrix of 3D point location would be obtained, which can constitute the uncertainty region of point location. Afterwards, we turned back to the error propagation of the primitive input errors in the stereo system and throughout the whole analysis process from primitive input errors to localization error. Our method has the same level of computational complexity as the state-of-the-art method. Finally, extensive experiments are performed to verify the performance of our methods.

Citation Download Citation

Yunting Li, Jun Zhang, and Jinwen Tian "Error analysis in stereo vision for location measurement of 3D point", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 981315 (14 December 2015); https://doi.org/10.1117/12.2203640

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