|Table of Contents|

Driverless vehicle positioning algorithm based on simultaneous positioning and mapping in low-visibility environment(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2022年03期
Page:
251-262
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Driverless vehicle positioning algorithm based on simultaneous positioning and mapping in low-visibility environment
Author(s):
GAO Yang1 CAO Wang-xin1 XIA Hong-yao1 ZHAO Yi-hui2
(1. School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China; 2. Xi'an Coal Mining Machinery Co., Ltd., Xi'an 710200, Shaanxi, China)
Keywords:
intelligent transportation environmental perception simultaneous localization and mapping low-light image enhancement noise suppression fusion positioning
PACS:
U491.2
DOI:
10.19818/j.cnki.1671-1637.2022.03.020
Abstract:
In order to achieve high-precision positioning for driverless vehicles in a large-scale and low-light environment, a fused positioning algorithm LVG_SLAM was proposed based on the system framework of the VINS-Mono algorithm. In LVG_SLAM, a RFAST low-light image enhancement module and a VG fusion positioning module were proposed and then added. The RFAST low-light image enhancement module applied a wavelet transform to separate the detailed information from the brightness information. In the RFAST module, the unified threshold and mean filter were applied to filter the detailed noisy information from the oringinal image while the bilateral texture filter algorithm was applied to enhance the detail information. After that, the multi-scale retinex algorithm was proposed to further enhance the contrast of the image to improve the success rate of corner extraction in a low-light environment, benefit from which, both the stability of image tracking and the robustness of the positioning algorithm were improved. Using an unscented Kalman filter(UKF)algorithm, the VG fusion positioning module loosely fused the positioning information from both the global navigation satellite system(GNSS)and the inertial navigation equipment. The fused positioning result was introduced as a constraint into the back end of the LVG_SLAM algorithm, benefit from which, the influence of cumulative error on the positioning accuracy of the algorithm was reduced by a joint nonlinear optimization. Analysis results show that compared with the VINS-Mono algorithm, the LVG_SLAM algorithm performs better on the EuRoC and Kitti public datasets, and the root mean square error reduces by 38.76% and 58.39%, respectively, so that the motion trajectory estimated by the LVG_SLAM algorithm is closer to the real trajectory. In an experiment of night road scene, the LVG_SLAM algorithm successfully constrains the positioning error into a certain range, and detects the closed loop, which greatly improves the positioning performance. The root mean square error, average error, maximum error, and median error reduce by 79.61%, 82.50%, 71.31%, and 83.77%, respectively. Compared with the VINS-Mono algorithm, the proposed LVG_SLAM algorithm has obvious advantages in both positioning accuracy and robustness. 4 tabs, 12 figs, 26 refs.

References:

[1] BRESSON G, ALSAYED Z, YU Li, et al. Simultaneous localization and mapping: a survey of current trends in autonomous driving[J]. IEEE Transactions on Intelligent Vehicles, 2017, 2(3): 194-220.
[2] DOMÍNGUEZ-CONTI J, YIN Jian-feng, ALAMI Y, et al. Visual-inertial SLAM initialization: a general linear formulation and a gravity-observing non-linear optimization[C]∥IEEE. 2018 IEEE International Symposium on Mixed and Augmented Reality(ISMAR). New York: IEEE, 2018: 37-45.
[3] HUANG Jia-wei, LIU Shi-guang. Robust simultaneous localization and mapping in low-light environment[J]. Computer Animation and Virtual Worlds, 2019, 30(3/4): 155-161.
[4] WANG Jun, WANG Rui, WU An-wen. Improved gamma correction for visual SLAM in low-light scenes[C]∥IEEE. 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference. New York: IEEE, 2019: 1159-1163.
[5] LORE K G, AKINTAYO A, SARKAR S. LLNet: a deep autoencoder approach to natural low-light image enhancement[J]. Pattern Recognition, 2017, 61: 650-662.
[6] WEI Chen, WANG Wen-jing, YANG Wen-han, et al. Deep retinex decomposition for low-light enhancement[J]. ArXiv, 2018, DOI: arXiv:1808.04560.
[7] DABOV K, FOI A, KATKOVNIK V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 2007, 16(8): 2080-2095.
[8] ZHANG Kai, ZUO Wang-meng, CHEN Yun-jin, et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising[J]. IEEE transactions on Image Processing, 2017, 26(7): 3142-3155.
[9] QIN Tong, LI Pei-liang, SHEN Shao-jie. VINS-mono: a robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020.
[10] ROESLER C, LARSON K M. Software tools for GNSS interferometric reflectometry(GNSS-IR)[J]. GPS Solutions, 2018, 22(3): 987-995.
[11] 章为川,孔祥楠,宋 文.图像的角点检测研究综述[J].电子学报,2015,43(11):2315-2321.
ZHANG Wei-chuan, KONG Xiang-nan, SONG Wen. Review of image corner detection algorithms[J]. Acta Electronica Sinica, 2015, 43(11): 2315-2321.(in Chinese)
[12] LUCAS B D, KANADE T. An iterative image registration technique with an application to stereo vision[C]∥IJCAI. Proceedings of the 7th International Joint Conference on Artificial Intelligence. Vancouver: IJCAI, 1981: 674-679.
[13] AGARWAL S, SNAVELY N, SEITZ S M, et al. Bundle adjustment in the large[C]∥Springer. 11th European Conference on Computer Vision. Berlin: Springer, 2010: 29-42.
[14] 唐崇武.图像统计建模与噪声分析关键技术的研究[D].上海:上海交通大学,2015.
TANG Chong-wu. Research onkey techniques of image statistical modeling and noise analysis[D]. Shanghai: Shanghai Jiao Tong University, 2015.(in Chinese)
[15] 郝志成,吴 川,杨 航,等.基于双边纹理滤波的图像细节增强方法[J].中国光学,2016,9(4):423-431.
HAO Zhi-cheng, WU Chuan, YANG Hang,et al. Image detail enhancement method based on multi-scale bilateral texture filter[J]. Chinese Optics, 2016, 9(4): 423-431.(in Chinese)
[16] LIN Hao-ming, SHI Zhen-wei. Multi-scale retinex improvement for nighttime image enhancement[J]. Optik, 2014, 125(24): 7143-7148.
[17] HAN Nian-long, HU Jin-xing, ZHANG Wei. Multi-spectral and SAR images fusion via Mallat and À trous wavelet transform[C]∥IEEE. 2010 18th International Conference on Geoinformatics. New York: IEEE, 2010: 1-4.
[18] 田流芳.基于中值滤波和小波变换的图像去噪算法研究[D].保定:河北大学,2014.
TIAN Liu-fang. Research onimage denoising algorithm based on median filtering and wavelet transform[D]. Baoding:Hebei University, 2014.(in Chinese)
[19] FEBIN I P, JIDESH P, BINI A A. A retinex-based variational model for enhancement and restoration of low-contrast remote-sensed images corrupted by shot noise[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 941-949.
[20] COSTA D S, MELLO C A B. Enhancement ofweakly illuminated images using CNN and retinex theory[C]∥IEEE. 2020 IEEE International Conference on Systems, Man, and Cybernetics. New York: IEEE, 2020: 2559-2564.
[21] MA Shi-ping, MA Hong-qiang, XU Yue-lei, et al. A low-light sensor image enhancement algorithm based on HSI color model[J]. Sensors, 2018, 18(10): 524-536.
[22] MUSTAFA W A, YAZID H, YAACOB S B. Illumination normalization of non-uniform images based on double mean filtering[C]∥IEEE. 2014 IEEE international conference on control system, computing and engineering. New York: IEEE, 2014: 366-371.
[23] 李晓飞.基于小波变换的图像去噪方法研究[D].南京:南京邮电大学,2016.
LI Xiao-fei. Research onimage denoising method based on wavelet transform[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2016.(in Chinese)
[24] FERNANDES L C, DE MENEZES L R A X, LOUREIRO A J F. Using the unscented transform to reduce the number of measurements in drive tests[J]. SN Applied Sciences, 2021, 3(2): 145-152.
[25] BURRI M, NIKOLIC J, GOHL P, et al. The EuRoC micro aerial vehicle datasets[J]. The International Journal of Robotics Research, 2016, 35(10): 1157-1163.
[26] GEIGER A, LENZ P, STILLER C, et al. Vision meets robotics: the KITTI dataset[J]. The International Journal of Robotics Research, 2013, 32(11): 1231-1237.

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Last Update: 2022-07-20