留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于室内标志的视觉定位方法

黄刚 蔡浩 邓超 何志 许宁波

黄刚, 蔡浩, 邓超, 何志, 许宁波. 基于室内标志的视觉定位方法[J]. 交通信息与安全.
引用本文: 黄刚, 蔡浩, 邓超, 何志, 许宁波. 基于室内标志的视觉定位方法[J]. 交通信息与安全.
HUANG Gang, CAI Hao, DENG Chao, HE Zhi, XU Ningbo. Indoor Sign-based Visual Localization Method[J]. Journal of Transport Information and Safety.
Citation: HUANG Gang, CAI Hao, DENG Chao, HE Zhi, XU Ningbo. Indoor Sign-based Visual Localization Method[J]. Journal of Transport Information and Safety.

基于室内标志的视觉定位方法

基金项目: 

国家自然科学基金青年基金项目(52002298)、湖北省自然科学基金青年项目(2020CFB118)、湖北省教育厅科学技术研究计划青年人才项目(Q20201107)资助

详细信息
    作者简介:

    黄刚(1989-),博士,讲师.研究方向:室内定位、智能汽车感知、场景建模.E-mail:ghuang@wust.edu.cn

    通讯作者:

    蔡浩(1989-),博士,讲师.研究方向:智能交通系统、安全辅助驾驶、智能车定位、驾驶行为分析.E-mail:caihao@wtu.edu.cn

Indoor Sign-based Visual Localization Method

  • 摘要: 为解决室内交通场景中智能汽车和移动机器人进行定位计算的问题,利用室内场景中已存在的各类标志,引入BEBLID(Boosted Efficient Binary Local Image Descriptor)算法,提出1种视觉定位方法。对BEBLID算法进行改进,赋予其对图像整体进行特征表征的能力。将定位过程分解为离线阶段和在线阶段,离线阶段构建场景标志地图,在线阶段将当前图像所提取的全局和局部BEBLID特征与场景标志地图的对应特征进行匹配,引入KNN方法确定最近节点和最近图像,并利用场景特征地图中存储的标志坐标信息,进行度量计算,获取当前位置信息。在教学楼、办公楼和室内停车场场景进行实验,实验中对场景标志的正确识别率达到90%,平均定位误差小于1 m,与传统方法相比,同一样本下识别精度相对提升约10%,实验验证了算法的有效性。

     

  • [1] YASSIN A, NASSER Y, AWAD M, et al. Recent advances in indoor localization:a survey on theoretical approaches and applications[J]. IEEE Communications Surveys & Tutorials, 2017, 19(99):1327-1346.
    [2] LI B, MUNOZ J P, RONG X, et al. Vision-based mobile indoor assistive navigation aid for blind people[J]. IEEE Transactions on Mobile Computing, 2019, 18(3):702-714.
    [3] ZOU H, CHEN C L, LI M, et al. Adversarial learning-enabled automatic WiFi indoor radio map construction and adaptation with mobile robot[J]. IEEE Internet of Things Journal, 2020, 7(8):6946-6954.
    [4] HUANG Y, ZHAO J, HE X, et al. Vision-based semantic mapping and localization for autonomous indoor parking[C]. 2018 IEEE Intelligent Vehicles Symposium(IV), Changshu, China:IEEE, 2018.
    [5] LAOUDIAS C, MOREIRA A, KIM S, et al. A survey of enabling technologies for network localization,tracking,and navigation[J]. IEEE Communications Surveys & Tutorials, 2018, 20(4):3607-3644
    [6] HERNÁNDEZ N, HUSSEIN A, CRUZADO D, et al. Applying low cost WiFi-based localization to in-campus autonomous vehicles[C]. 2017 IEEE 20th International Conference on Intelligent Transportation Systems(ITSC), Yokohama, Japan:IEEE, 2017.
    [7] 赵国旗, 杨明, 王冰, 等.基于智能终端的移动机器人室内外无缝定位方法[J].上海交通大学学报, 2018, 52(1):13-19. ZHAO Guoqi, YANG Ming, WANG Bing, et al. Mobile robot seamless localization based on smart device in indoor and outdoor environments[J]. Journal of Shanghai Jiaotong University, 2018, 52(1):13-19.(in Chinese)
    [8] WANG W, MARELLI D, FU M. Multiple-vehicle localization using maximum likelihood Kalman filtering and ultra-wideband signals[J]. IEEE Sensors Journal, 2021, 21(4):4949-4956.
    [9] 王博远, 刘学林, 蔚保国, 等.WiFi指纹定位中改进的加权k近邻算法[J].西安电子科技大学学报, 2019, 46(5):41-47. WANG Boyuan, LIU Xuelin, YU Baoguo, et al. Improved weighted k-nearest neighbor algorithm for wifi fingerprint positioning[J]. Journal of Xidian University,2019,46(5):41-47.(in Chinese)
    [10] 杨保, 张鹏飞, 李军杰, 等.一种基于蓝牙的室内定位导航技术[J].测绘科学, 2019, 44(6):89-95. YANG Bao, ZHANG Pengfei, LI Junjie, et al. An indoor positioning and navigation technology based on bluetooth[J]. Science of Surveying and Mapping, 2019, 44(6):89-95.(in Chinese)
    [11] SADRUDDIN H, MAHMOUD A, ATIA M M. Enhancing body-mounted LiDAR SLAM using an IMU-based pedestrian dead reckoning(PDR) model[C]. 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS), Springfield, MA, USA:IEEE, 2020.
    [12] CAMPOS C,ELVIRA R,RODRÍGUEZ J J G,et al. ORB-slam3:an accurate open-source library for visual, visual-inertial, and multi-map SLAM[J]. IEEE Transactions on Robotics, 2021, Early Access:1-17.
    [13] RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB:an efficient alternative to SIFT or SURF[C]. IEEE International Conference on Computer Vision, Barcelona, Spain:IEEE, 2011.
    [14] MUR-ARTALR, MONTIELJMM, TARDOSJD.ORB-SLAM:a versatile and accurate monocular SLAM system[J]. IEEE Transactions on Robotics, 2015, 31(5):1147-1163.
    [15] 胡月志, 李娜, 胡钊政, 等.基于ORB全局特征与最近邻的交通标志快速识别算法[J].交通信息与安全, 2016(1):23-29. HU Yuezhi, LI Na, HU Zhaozheng, et al. Fast sign recognition based on ORB holistic feature and k-nearest neighbor method[J]. Journal of Transport Information and Safety, 2016(1):23-29.(in Chinese)
    [16] 陶倩文, 胡钊政, 黄刚, 等.基于消防安全疏散标志的高精度室内视觉定位[J].交通信息与安全, 2018, 36(02):39-46+60. TAO Qianwen,HU Zhaozheng,HUANG Gang,et al.High-accuracy vision-based indoor positioning using building safety evacuation signs. Journal of Transport Information and Safety, 2018, 36(2):39-46+60.(in Chinese)
    [17] BAY H, TUYTELAARS T, VAN GOOL L. Surf:speeded up robust features[C]. European Conference on Computer Vision, Graz, Austria:ECCV, 2006.
    [18] ELLOUMI W, LATOUI A, CANALS R, et al. Indoor pedestrian localization with a smartphone:a comparison of inertial and vision-based methods[J]. IEEE Sensors Journal, 2016, 16(13):5376-5388.
    [19] SUÁREZ I,SFEIR G,BUENAPOSADA J M,et al. BEBLID:boosted efficient binary local image descriptor[J]. Pattern Recognition Letters, 2020(133):366-372.
    [20] Zhang ZY. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11):1330-1334.
  • 加载中
计量
  • 文章访问数:  4007
  • HTML全文浏览量:  62
  • PDF下载量:  350
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-05-23
  • 网络出版日期:  2021-12-14

目录

    /

    返回文章
    返回