A Method of Loop Closure Detection Improved by Bag-of-Visual Words Based on Original-Illumination Invariant Image

被引：0
|
作者：
Hu Z.-F. [1 ]
Zeng N.-W. [1 ]
Luo Y. [1 ]
Xiao Y.-T. [1 ]
Zhong Z.-Y. [1 ]
机构：
[1] School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2021年 / 50卷 / 04期
关键词：
Bag-of-visual words; Illumination invariant image; Loop closure detection; Simultaneous localization and mapping;
D O I：
10.12178/1001-0548.2020272
中图分类号：
学科分类号：
摘要：
When the ambient light of the robot changes, the performance of the loop closure detection algorithm based on the traditional visual word bag will decrease, and it is prone to perceptual aliasing and perceptual variation, thus judging the false closed-loop. In this paper, the original color image is used to generate an illumination invariant image related only to the light source, and then a visual dictionary of the original illumination invariant image is generated. For each image, two histograms and similarity scores are calculated to determine whether it is a closed loop. Finally, it is tested on the data set. The experimental results show that compared with the bag-of-words (BoW), the loop closure detection algorithm proposed in this paper has better robustness to the changes in the environment. Copyright ©2020 Journal of University of Electronic Science and Technology of China. All rights reserved.
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页码：586 / 591
页数：5
相关论文
共 16 条
  • [1] SAPUTRA M R U, MARKHAM A, TRIGONI N., Visual SLAM and structure from motion in dynamic environments: A survey, ACM Computing Surveys, 51, 2, pp. 1-36, (2018)
  • [2] LIU Qiang, DUAN Fu-hai, SANG Yong, Et al., A survey of loop-closure detection method of visual SLAM in complex environments, Robot, 41, 1, pp. 112-123, (2019)
  • [3] DING Jie, FANG Yong-chun, Multi-strategy based exploration for 3D mapping in unknown environments using a mobile robot, 2019 Chinese Control Conference (CCC), pp. 4732-4738, (2019)
  • [4] GAO Xiang, ZHANG Tao, Unsupervised learning to detect loops using deep neural networks for visual SLAM system, Autonomous Robots, 41, 1, pp. 1-18, (2017)
  • [5] GALVEZ-LO PEZ D, TARDOS J D., Bags of binary words for fast place recognition in image sequences, IEEE Transactions on Robotics, 28, 5, pp. 1188-1197, (2012)
  • [6] ANGELI A, FILLIAT D, DONCIEUX S, Et al., Fast and incremental method for loop-closure detection using bags of visual words, IEEE Transactions on Robotics, 24, 5, pp. 1027-1037, (2008)
  • [7] CHEN J, LI J, XU Y, Et al., A compact loop closure detection based on spatial partitioning, International Conference on Image Vision and Computing, pp. 371-375, (2017)
  • [8] NOWICKI M, WIETRZYKOWSKI J, SKRZYPCZYNSKI P., Experimental evaluation of visual place recognition algorithms for personal indoor localization, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), (2016)
  • [9] JIE W, ALPER Y, LEI Y., DCF-BoW: Build match graph using bag of deep convolutional features for structure from motion, IEEE Geoscience and Remote Sensing Letters, 99, pp. 1-5, (2018)
  • [10] KYI P, YUTTANA K., Biomedical images stitching using ORB feature based approach, 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), (2018)
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