Application of improved BEMD threshold denoising algorithm in monocular visual odometer

被引：0
|
作者：
Liu D. [1 ]
Chen X. [1 ]
Fang W. [1 ]
机构：
[1] Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, School of Instrument Science and Engineering, Southeast University, Nanjing
来源：
Chen, Xiyuan (chxiyuan@seu.edu.cn) | 2018年 / Editorial Department of Journal of Chinese Inertial Technology卷 / 26期
关键词：
Bidimensional empirical mode decomposition; Image denoising; Monocular visual location; Wavelet denoising;
D O I：
10.13695/j.cnki.12-1222/o3.2018.06.007
中图分类号：
学科分类号：
摘要：
Aiming at the problem that the image acquired by monocular visual positioning system contains large noises, an improved threshold denoising algorithm for bidimensional empirical mode decomposition (BEMD) is proposed. First, the position sensitivity error of the BEMD is eliminated by constructing the noise- compressed image. Then, the noise-compressed image is decomposed into a series of intrinsic mode functions (IMFs) by the BEMD, which are separated into signal-dominant IMFs and noise-dominant IMFs by using a similarity measure based on 𝓁2-norm and a probability density function, and a soft thresholding technique is used adaptively to remove the noise inherent in noise-dominant IMFs. Finally, the algorithm is applied to the monocular visual location and compared with the wavelet denoising algorithm and the BEMD-DT one. The comparison results show that, in both cases of turning and linear motion, the east, north and heading errors of the proposed algorithm are improved by 74%, 64% and 54% respectively compared to that of the Sym4 wavelet denoising algorithm, and are improved by 60%, 48% and 39% respectively compared to that of the Db6 wavelet denoising algorithm, and are improved by 73%, 96% and 84% respectively compared to that of the BEMD-DT algorithm, which show the superiority of the proposed algorithm. © 2018, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.
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页码：737 / 746
页数：9
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