LIU Jingbin, WANG Zemin, LÜ Xuanfan, LI Wei, YIN Fei, QIU Hongyu. Indoor Ego‐Localization Method for Low Cost Millimeter Wave Radar[J]. Geomatics and Information Science of Wuhan University, 2023, 48(9): 1399-1408. DOI: 10.13203/j.whugis20210593
Citation: LIU Jingbin, WANG Zemin, LÜ Xuanfan, LI Wei, YIN Fei, QIU Hongyu. Indoor Ego‐Localization Method for Low Cost Millimeter Wave Radar[J]. Geomatics and Information Science of Wuhan University, 2023, 48(9): 1399-1408. DOI: 10.13203/j.whugis20210593

Indoor Ego‐Localization Method for Low Cost Millimeter Wave Radar

More Information
  • Received Date: October 11, 2022
  • Available Online: July 21, 2022
  • Objectives 

    Millimeter wave radar has been widely used in automotive industry and other fields, but its application is mainly limited to the environmental perception of obstacles or specific tasks. At pres‍ent, there is little research on the application of millimeter wave radar in the field of navigation and positioning.

    Methods 

    This paper first studies the raw data processing principle of millimeter wave radar, and then designs an indoor ego‐localization method which only depends on a low‐cost millimeter wave radar. The process mainly includes extracting centroid feature points using density based spatial clustering of applications with noise(DBSCAN) algorithm, matching centroid feature point pairs through nearest neighbor criterion, constructing nonlinear optimization function and solving positioning results using levenberg marquardt method.

    Results and Conclusions 

    Experiments show that indoor navigation and positioning can be solved in real time by using a low‐cost millimeter wave radar. Under static conditions, the average horizontal positioning accuracy can reach sub centimeter level (mean value is 0.82 cm and standard deviation is 0.47 cm). Under dynamic conditions, the absolute trajectory error can reach 0.66 m and the average head‍ing angle error can reach 4.58°, which shows the feasibility of ego‐localization of low‐cost millimeter wave radar. Finally, this paper discusses the problems and feasible research ideas of low‐cost millimeter wave radar in navigation and positioning.

  • [1]
    陈锐志, 叶锋. 基于Wi‐Fi信道状态信息的室内定位技术现状综述[J]. 武汉大学学报(信息科学版), 2018, 43(12): 2064-2070. doi: 10.13203/j.whugis20180176

    Chen Ruizhi, Ye Feng. An Overview of Indoor Positioning Technology Based on Wi‐Fi Channel State Information[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 2064-2070 doi: 10.13203/j.whugis20180176
    [2]
    柳景斌, 黄百川, 张斌, 等. 利用双天线商用Wi‐Fi信道状态信息估计到达角[J]. 武汉大学学报(信息科学版), 2018, 43(12): 2167-2172. doi: 10.13203/j.whugis20180178

    Liu Jingbin, Huang Baichuan, Zhang Bin, et al. AOA Estimation Based on Channel State Information Extracted from Wi‐Fi with Double Antenna[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 2167-2172 doi: 10.13203/j.whugis20180178
    [3]
    陈锐志, 郭光毅, 叶锋, 等. 智能手机音频信号与MEMS传感器的紧耦合室内定位方法[J]. 测绘学报, 2021, 50(2): 143-152. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202102002.htm

    Chen Ruizhi, Guo Guangyi, Ye Feng, et al. Tightly‐Coupled Integration of Acoustic Signal and MEMS Sensors on Smartphones for Indoor Position‍ing[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(2): 143-152 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB202102002.htm
    [4]
    王锐. 基于行人航位推算(PDR)技术的室内导航系统[D]. 南京: 南京邮电大学, 2019.

    Wang Rui. A Pedestrian Dead Reckoning (PDR) Based Indoor Navigation System[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2019
    [5]
    Campos C, Elvira R, Rodríguez J J G, et al. ORB‐SLAM3: An Accurate Open‐Source Library for Visual, Visual‐Inertial, and Multimap SLAM[J]. IEEE Transactions on Robotics, 2021, 37(6): 1874-1890. doi: 10.1109/TRO.2021.3075644
    [6]
    Mur‐Artal R, Montiel J M M, Tardós J D. ORB‐SLAM: A Versatile and Accurate Monocular SLAM System[J]. IEEE Transactions on Robotics, 2015, 31(5): 1147-1163. doi: 10.1109/TRO.2015.2463671
    [7]
    Qin T, Li P L, Shen S J. VINS‐Mono: A Robust and Versatile Monocular Visual‐Inertial State Estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020. doi: 10.1109/TRO.2018.2853729
    [8]
    Ji Z, Singh S. Visual‐LiDAR Odometry and Mapping: Low‐Drift, Robust, and Fast[C]// IEEE International Conference on Robotics & Automation, Seattle, WA, USA, 2015.
    [9]
    Wang Z, Zhang Q, Li J, et al. A Computationally Efficient Semantic SLAM Solution for Dynamic Scenes[J]. Remote Sensing, 2019, 11(11): 1363. doi: 10.3390/rs11111363
    [10]
    Barnes D, Gadd M, Murcutt P, et al. The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset[C]// IEEE International Conference on Robotics and Automation, Paris, France, 2020.
    [11]
    Cen S H, Newman P. Precise Ego‐Motion Estimation with Millimeter‐Wave Radar Under Diverse and Challenging Conditions[C]// IEEE International Conference on Robotics and Automation, Brisbane, Australia, 2018.
    [12]
    Cen S H, Newman P. Radar‐Only Ego‐Motion Estimation in Difficult Settings via Graph Matching[C]// International Conference on Robotics and Automation, Montreal, Canada, 2019.
    [13]
    Hong Z, Petillot Y, Wang S. Radar SLAM: Radar Based Large‐Scale SLAM in All Weathers[C]//IEEE International Conference on Intelligent Robots and Systems, Las Vegas, USA, 2020.
    [14]
    Kung P C, Wang C C, Lin W C. A Normal Distribution Transform‐Based Radar Odometry Designed for Scanning and Automotive Radars[C]// IEEE International Conference on Robotics and Automation, Xi􀆳an, China, 2021.
    [15]
    Burnett K, Schoellig A P, Barfoot T D. Do We Need to Compensate for Motion Distortion and Doppler Effects in Spinning Radar Navigation?[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 771-778. doi: 10.1109/LRA.2021.3052439
    [16]
    郑睿, 李方东. 基于调频毫米波的安防移动机器人导航系统[J]. 仪器仪表学报, 2021, 42(3): 105-113. doi: 10.19650/j.cnki.cjsi.J2007261

    Zheng Rui, Li Fangdong. Navigation System of Security Mobile Robot Based on FM Millimeter Wave[J]. Chinese Journal of Scientific Instrument, 2021, 42(3): 105-113 doi: 10.19650/j.cnki.cjsi.J2007261
    [17]
    王彦平, 刘宇通, 李洋, 等. 基于CSM的毫米波雷达点云匹配定位方法[C]//第十四届全国信号和智能信息处理与应用学术会议, 中国, 北京, 2021.

    Wang Yanping, Liu Yutong, Li Yang, et al. PointCloud Matching Localization Method for Millimeter Wave Radar Based on CSM[C]// National Confer‍ence on Signal and Intelligent Information Process‍ing and Application, China, Beijing, 2021
    [18]
    Ester M, Kriegel H, Sander J, et al. A Density‐Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]//SIGKDD, Portland, Oregon, USA, 1996.
    [19]
    Moré J J. The Levenberg‐Marquardt Algorithm: Implementation and Theory[M]//Lecture Notes in Mathematics. Berlin, Germany: Springer, 1978.
    [20]
    Hess W, Kohler D, Rapp H, et al. Real‐Time Loop Closure in 2D LiDAR SLAM[C]//IEEE International Conference on Robotics and Automation, Stockholm, Sweden, 2016.
    [21]
    Sturm J, Engelhard N, Endres F, et al. A Benchmark for the Evaluation of RGB‐D SLAM Systems[C]// IEEE International Conference on Intelligent Robots and Systems, Vilamoura‐Algarve, Portugal, 2012.

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