Research on the robust positioning model of ORB-SLAM2 in outdoor glare scene

doi:10.13474/j.cnki.11-2246.2023.0265

Abstract

Abstract: In order to solve the problem of low stability of visual SLAM in outdoor glare scenes, combined with the method of deep learning in the field of image processing, an improved ORB-SLAM2 algorithm for outdoor scenes containing glare interference is proposed. Firstly, the images taken by the positioning camera are fed into the pre-trained U-Net network for processing, which can effectively remove the glare of the pictures, while maintaining the original information output for the images without glare interference. Secondly, the image brightness judgment algorithm used to judge the brightness of the output images of the network, and the contrast limited histogram equalization is used for the low-brightness images. According to the experimental results in actual outdoor scenes, the motion trajectory of the improved ORB-SLAM2 system is closer to the real motion trajectory of the camera than the original system, and the system has better robustness in outdoor glare scenes.

Key words: visual SLAM, glare, U-Net, CLAHE, robustness

CLC Number:

P228

WU Siqi, LIU Fei, BAI Yu, MA Yuntao, WANG Fei, GUO Ziyu. Research on the robust positioning model of ORB-SLAM2 in outdoor glare scene[J]. Bulletin of Surveying and Mapping, 2023, 0(9): 59-63.

References

[1] 刘浩敏,章国锋,鲍虎军.基于单目视觉的同时定位与地图构建方法综述[J].计算机辅助设计与图形学学报,2016,28(6):855-868.
[2] BRESSON G,ALSAYED Z,YU L,et al.Simultaneous localizations and mapping:a survey of current trends in autonomous driving[J].IEEE Transactions on Intelligent Vehicles,2017,2(3):194-220.
[3] NEWCOMBE R A,LOVEGROVE S J,DAVISON A J.DTAM:dense tracking and mapping in real-time[C]//Proceedings of 2011 International Conference on Computer Vision.Los Alamitos:IEEE Computer Society Press,2011:2320-2327.
[4] WU X,PRADALIER C.Illumination robust monocular direct visual odometry for outdoor environment mapping[C]//Proceedings of 2019 International Conference on Robotics and Automation.[S.l.]:ICRA,2019:2392-2398.
[5] MUR-ARTAL R,MONTIEL J M M,TARDOS J D.ORB-SLAM:a versatile and accurate monocular SLAM system[EB/OL].[2022-09-20].https://arxiv.org/abs/1502.00956.
[6] 王林生.防眩光用水性聚氨酯涂层涂料的合成与性能研究[D].北京:北京化工大学,2013.
[7] ASHA C S,BHAT S K,NAYAK D,et al.Auto removal of bright spot from images captured against flashing light source[C]//Proceedings of 2019 IEEE International Conference on Distributed Computing,VLSI,Electrical Circuits and Robotics (DISCOVER).[S.l.]:IEEE,2019:1-6.
[8] CHABERT F.Automated lens flare removal[R].[S.l.]:Stanford University,Department of Electrical Engineering, 2015.
[9] VITORIA P,BALLESTER C.Automatic flare spot artifact detection and removal in photographs[J].Journal of Mathematical Imaging and Vision,2019,61(4):515-533.
[10] FAN Qingnan,YANG Jiaolong,HUA Gang,et al.A generic deep architecture for single image reflection removal and image smoothing[EB/OL].[2022-09-15].https://arxiv.org/abs/1708.03474.
[11] LI C,YANG Y X,HE K,et al.Single image reflection removal through cascaded refinement[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE,2020:3565-3574.
[12] ZHANG X E,NG R,CHE Q F.Single image reflection separation with perceptual losses[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE,2018:4786-4794.
[13] QIAN R,TAN R T,YANG W H,et al.Attentive generative adversarial network for raindrop removal from a single image[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE,2018:2482-2491.
[14] WEI W,MENG D Y,ZHAO Q,et al.Semi-supervised transfer learning for image rain removal[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE,2019:3877-3886.
[15] WU Y C,HE Q R,XUE T F,et al.How to Train Neural Networks for Flare Removal[C]//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision.[S.l.]:IEEE,2021:2239-2247.
[16] DAI Y,LI C,ZHOU S,et al.Flare7K:a phenomenological nighttime flare removal dataset[EB/OL].[2022-09-30].https://doi.org/10.48550/arXiv.2210.06570.