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Journal of Sensors
Volume 2016, Article ID 4132721, 9 pages
http://dx.doi.org/10.1155/2016/4132721
Research Article

An Analytical Measuring Rectification Algorithm of Monocular Systems in Dynamic Environment

1Electronic Information School, Wuhan University, Wuhan, Hubei 430072, China
2Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China

Received 16 October 2015; Revised 25 January 2016; Accepted 2 March 2016

Academic Editor: Yassine Ruichek

Copyright © 2016 Deshi Li and Xiaoliang Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Range estimation is crucial for maintaining a safe distance, in particular for vision navigation and localization. Monocular autonomous vehicles are appropriate for outdoor environment due to their mobility and operability. However, accurate range estimation using vision system is challenging because of the nonholonomic dynamics and susceptibility of vehicles. In this paper, a measuring rectification algorithm for range estimation under shaking conditions is designed. The proposed method focuses on how to estimate range using monocular vision when a shake occurs and the algorithm only requires the pose variations of the camera to be acquired. Simultaneously, it solves the problem of how to assimilate results from different kinds of sensors. To eliminate measuring errors by shakes, we establish a pose-range variation model. Afterwards, the algebraic relation between distance increment and a camera’s poses variation is formulated. The pose variations are presented in the form of roll, pitch, and yaw angle changes to evaluate the pixel coordinate incensement. To demonstrate the superiority of our proposed algorithm, the approach is validated in a laboratory environment using Pioneer 3-DX robots. The experimental results demonstrate that the proposed approach improves in the range accuracy significantly.