Navigation system based on machine vision of multiple reference markers

Xiaopeng Su; Wenbo Dong; Zhenyu Wang; Yuanyuan Zhou

doi:10.1117/12.2295880

15 November 2017 Navigation system based on machine vision of multiple reference markers

Xiaopeng Su, Wenbo Dong, Zhenyu Wang, Yuanyuan Zhou

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106053V (2017) https://doi.org/10.1117/12.2295880
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

Abstract

The position and attitude measurement of space object is a key problem in the field of real-time navigation, modern control and motion tracking. As a non-contact position and attitude estimation method, machine vision position and attitude estimation has the advantages of simple structure and convenient measurement. This paper presents a vision positioning system and method based on multiple reference markers. The camera moving along the object continuously collects images containing reference markers from the camera's field of view．The spatial position information of reference mark is determined in advance, and the position and direction of moving target are calculated according to location and attitude algorithm. The main contribution of this paper: first, a plurality of reference markers is arranged in the range of moving objects so as to enlarge the range of visual positioning; second, when more than one reference marker appears in the field of view, it is possible to improve the positioning accuracy by selecting the marker of the larger contour area or the marker of the distance closer to the imaging plane principal point; third, we use the decoder to transform the reference marker into digital number. This method can improve the robustness of the system.

Citation Download Citation

Xiaopeng Su, Wenbo Dong, Zhenyu Wang, and Yuanyuan Zhou "Navigation system based on machine vision of multiple reference markers", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106053V (15 November 2017); https://doi.org/10.1117/12.2295880

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