Solar Meridian Extraction Method Based on Hough Transformation

Yan, 崔岩 Cui; Xinchang, 周鑫昌 Zhou; Yafei, 刘亚飞 Liu; Kang, 刘康 Liu; Jinkui, 褚金奎 Chu

doi:10.3788/aos202040.1701002

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2015, Lu et al proposed a solar meridian extraction method based on Hough transform, [9] and in 2020 Cui et al compared it with LSM, and the accuracy was improved. [10] In 2017, Zhang et al used polarization-light field camera to obtain atmospheric polarization patterns, proposed a symmetry evaluation method of polarization Angle, obtained the optimal solar meridian fitting results by minimizing symmetry errors, and conducted polarized light orientation experiments in a cloudy environment to verify the effectiveness of this method [11].…”

Section: Introductionmentioning

confidence: 99%

Bio-inspired Sky Polarized Orientation based on the atmospheric polarization symmetry mode and Inertial Sensors

Chen,

Teng,

Zhou

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

This paper presents a sky polarized orientation method based on the symmetry of the atmospheric polarization mode and the inertial sensors. Firstly, a method of extracting solar meridians based on canny operator and Hough Transform is proposed by using the symmetry of atmospheric polarization pattern. Then using the horizontal Angle information of the inertial sensor and the ephemeris information of the sun, a method for solving the yaw Angle of the polarized light is designed. Finally, a single axis rotation experiment and a vehicle-mounted dynamic experiment are carried out to verify and compare the proposed method. Compared with the least square method, the positioning accuracy of the single-axis turntable experiment is improved by 22.61%~73.86%, and that of the vehicle-mounted dynamic experiment is improved by 47.71%, which shows that the method can provide a new orientation means for unmanned vehicles and unmanned aerial vehicles.

show abstract

Section: Introductionmentioning

confidence: 99%