一种基于改进CoHOG的视觉SLAM算法

doi:10.13474/j.cnki.11-2246.2022.0355

测绘通报 ›› 2022, Vol. 0 ›› Issue (12): 42-50.doi: 10.13474/j.cnki.11-2246.2022.0355

一种基于改进CoHOG的视觉SLAM算法

于尧^1,2, 孙新柱^1,2, 郭俊阳^1,2, 陈孟元^1,2

1. 安徽工程大学电气工程学院, 安徽芜湖 241000;
2. 高端装备先进感知与智能控制教育部重点实验室, 安徽芜湖 241000

收稿日期:2021-12-24 出版日期:2022-12-25 发布日期:2023-01-05
通讯作者: 孙新柱。E-mail:xzsun@ahpu.edu.cn
作者简介:于尧(1996-),男,硕士生,研究方向为智能信息处理与应用。E-mail:173077286@qq.com
基金资助:
国家自然科学基金（61903002）；安徽省高校协同创新项目（GXXT-2021-050）；芜湖市科技计划（2020yf59）

Visual SLAM algorithm based on improved CoHOG

YU Yao^1,2, SUN Xinzhu^1,2, GUO Junyang^1,2, CHEN Mengyuan^1,2

1. College of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China;
2. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Wuhu 241000, China

Received:2021-12-24 Online:2022-12-25 Published:2023-01-05

摘要/Abstract

摘要： 移动机器人在SLAM的闭环检测环节计算量大、运行时间长、匹配误差大，从而导致闭环检测精度较低。针对该问题，本文在CoHOG闭环检测算法的基础上进行改进，将算法中的HOG描述符改进为GDF-HOG描述符，以增强图像特征表现，提高图像特征提取效率；在匹配环节前添加GDF-HOG全局粗匹配，以减少视觉模板的数量，提高算法的计算效率；在匹配环节后添加感兴趣区域（ROI）位置匹配进行检验，以减少闭环检测的假阳性，提高准确率。将本文闭环检测算法与RatSLAM相结合，在公开数据集与真实环境中进行测试，测试结果表明，本文算法在闭环检测环节的准确率较高，且对环境的适应能力较强。

关键词: 图像识别及其装置, 视觉位置识别, CoHOG算法, 闭环检测, RatSLAM

Abstract: The mobile robot has a large amount of calculation, long running time and low accuracy of matching in the closed-loop detection link of SLAM. So this paper improves the CoHOG closed-loop detection algorithm, improves the HOG descriptor in the algorithm to GDF-HOG descriptor, to enhance image characteristics and improve the efficiency of image feature extraction. Before the matching process, GDF-HOG global rough matching is added to reduce the number of visual templates and improve the computational efficiency of the algorithm. After the matching process, region of interest (ROI) position matching is added for testing to reduce false positives of closed-loop detection and improve accuracy.Finally, the closed-loop detection algorithm proposed in this paper is combined with RatSLAM, and tested in open data sets and real environments. The test results show that the proposed algorithm has advantages in the accuracy of closed-loop detection and adaptability to the environment.

Key words: image recognition and apparatus, visual place recognition, CoHOG algorithm, closed-loop detection, RatSLAM

中图分类号:

P237

于尧, 孙新柱, 郭俊阳, 陈孟元. 一种基于改进CoHOG的视觉SLAM算法[J]. 测绘通报, 2022, 0(12): 42-50.

YU Yao, SUN Xinzhu, GUO Junyang, CHEN Mengyuan. Visual SLAM algorithm based on improved CoHOG[J]. Bulletin of Surveying and Mapping, 2022, 0(12): 42-50.

参考文献

[1] 李国竣, 徐延海, 段杰文, 等.利用局部自适应阈值方法提取ORB-SLAM特征点[J].测绘通报, 2021(9):32-36.
[2] 盛超, 潘树国, 赵涛, 等.基于图像语义分割的动态场景下的单目SLAM算法[J].测绘通报, 2020(1):40-44.
[3] NICHOLSON L, MILFORD M, SVNDERHAUF N.QuadricSLAM:dual quadrics from object detections as landmarks in object-oriented SLAM[J].IEEE Robotics and Automation Letters, 2019, 4(1):1-8.
[4] MOLLOY T L, FISCHER T, MILFORD M, et al.Intelligent reference curation for visual place recognition via Bayesian selective fusion[J].IEEE Robotics and Automation Letters, 2021, 6(2):588-595.
[5] CHEN Zetao, LIU Lingqiao, SA I, et al.Learning context flexible attention model for long-term visual place recognition[J].IEEE Robotics and Automation Letters, 2018, 3(4):4015-4022.
[6] FISCHER T, MILFORD M.Event-based visual place recognition with ensembles of temporal windows[J].IEEE Robotics and Automation Letters, 2020, 5(4):6924-6931.
[7] 刘强, 段富海, 桑勇, 等.复杂环境下视觉SLAM闭环检测方法综述[J].机器人, 2019, 41(1):112-123.
[8] NG P C, HENIKOFF S.SIFT:predicting amino acid changes that affect protein function[J].Nucleic Acids Research, 2003, 31(13):3812-3814.
[9] BAY H, ESS A, TUYTELAARS T, et al.Speeded-up robust features (SURF)[J].Computer Vision and Image Understanding, 2008, 110(3):346-359.
[10] 张良桥, 陈国良, 许晓东, 等.一种用于图像特征提取的改进ORB-SLAM算法[J].测绘通报, 2019(3):16-20.
[11] 刘国忠, 胡钊政.基于SURF和ORB全局特征的快速闭环检测[J].机器人, 2017, 39(1):36-45.
[12] 张云洲, 胡航, 秦操, 等.基于栈式卷积自编码的视觉SLAM闭环检测[J].控制与决策, 2019, 34(5):981-988.
[13] 龚希, 吴亮, 谢忠, 等.融合全局和局部深度特征的高分辨率遥感影像场景分类方法[J].光学学报, 2019, 39(3):19-29.
[14] 牛杰, 卜雄洙, 钱堃, 等.一种融合全局及显著性区域特征的室内场景识别方法[J].机器人, 2015, 37(1):122-128.
[15] CHEN Z, LAM O, JACOBSON A, et al.Convolutional neural network-based place recognition[C]//Proceedings of 2014 Conference on Robotics and Automati-on.Melbourne:[s.n.], 2014.
[16] KHALIQ A, EHSAN S, CHEN Zetao, et al.A holistic visual place recognition approach using lightweight CNNs for significant ViewPoint and appearance changes[J].IEEE Transactions on Robotics, 2020, 36(2):561-569.
[17] MCMANUS C, UPCROFT B, NEWMANN P.Scene signatures:localised and point-less features for localisation[C]//Proceedings of Conference on Robotics:Science and Systems.[S.l.]:Science and Systems Foundation, 2014.
[18] CHEN Zetao, JACOBSON A, SVNDERHAUF N, et al.Deep learning features at scale for visual place recognition[C]//Proceedings of 2017 IEEE International Conference on Robotics and Automation.Singapore:IEEE, 2017:3223-3230.
[19] ARANDJELOVIĆ R, GRONAT P, TORII A, et al.NetVLAD:CNN architecture for weakly supervised place recognition[C]//Proceedings of 2016 IEEE Transactions on Pattern Analysis and Machine Intelligence.[S.l.]:IEEE, 2016:1437-1451.
[20] CHEN Zetao, MAFFRA F, SA I, et al.Only look once, mining distinctive landmarks from ConvNet for visual place recognition[C]//Proceedings of 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Vancouver, BC, Canada:IEEE, 2017:9-16.
[21] SHEKHAR R, JAWAHAR C V.Word image retrieval using bag of visual words[C]//Proceedings of the 10th IAPR International Workshop on Document Analysis Systems.Gold Coast, QLD, Australia:IEEE, 2012:297-301.
[22] ZAFFAR M, EHSAN S, MILFORD M, et al.CoHOG:a light-weight, compute-efficient, and training-free visual place recognition technique for changing environments[J].IEEE Robotics and Automation Letters, 2020, 5(2):1835-1842.
[23] HANBAY K, ALPASLAN N, TALU M F, et al.Continuous rotation invariant features for gradient-based texture classification[J].Computer Vision and Image Understanding, 2015, 132:87-101.
[24] GUCLU O, CAN A B.Histogram based visual place recognition for improving SLAM performance[C]//Proceedings of 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC).Bragança, Portugal:IEEE, 2016:174-180.
[25] PAZ L M, PINIÉS P, TARDÓS J D, et al.Large-scale 6-DOF SLAM with stereo-in-hand[J].IEEE Transactions on Robotics, 2008, 24(5):946-957.
[26] SCARAMUZZA D, FRAUNDORFER F.Visual odometry tutorial[J].IEEE Robotics & Automation Magazine, 2011, 18(4):80-92.

一种基于改进CoHOG的视觉SLAM算法

Visual SLAM algorithm based on improved CoHOG

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