A Fusion Localization System for Security Robots Based on Millimeter Wave Radar and Inertial Sensors

被引:1
作者
Zheng, Rui [1 ]
Sun, Geng [1 ]
Li, Fang Dong [2 ]
机构
[1] Anhui Normal Univ, Coll Phys & Elect Informat, Wuhu 241002, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 210016, Peoples R China
关键词
security robot; Kalman filtering; millimeter wave; localization; inertial sensor; NAVIGATION;
D O I
10.3390/s24237551
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
In smoggy and dusty environments, vision- and laser-based localization methods are not able to be used effectively for controlling the movement of a robot. Autonomous operation of a security robot can be achieved in such environments by using millimeter wave (MMW) radar for the localization system. In this study, an approximate center method under a sparse point cloud is proposed, and a security robot localization system based on millimeter wave radar is constructed. To improve the localization accuracy of the robot, inertial localization of the robot is integrated with MMW radar. Based on the concept of inertial localization, the state equation for the motion principle of the robot is deduced. According to principle of MMW localization, the measurement equation is derived, and a kinematics model of the robot is constructed. Further, by applying the Kalman filtering algorithm, a fusion localization system of the robot based on MMWs and inertial localization is proposed. The experimental results show that with iterations of the filtering algorithm, the gain matrix converges gradually, and the error of the fusion localization system decreases, leading to the stable operation of the robot. Compared to the localization system with only MMW radar, the average localization error is approximately reduced from 11 cm to 8 cm, indicating that the fusion localization system has better localization accuracy.
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页数:18
相关论文
共 28 条
[1]   Low-Cost Reduced Navigation System for Mobile Robot in Indoor/Outdoor Environments [J].
Al Khatib, Ehab I. ;
Jaradat, Mohammad Abdel Kareem ;
Abdel-Hafez, Mamoun F. .
IEEE ACCESS, 2020, 8 :25014-25026
[2]   Milli-RIO: Ego-Motion Estimation With Low-Cost Millimetre-Wave Radar [J].
Almalioglu, Yasin ;
Turan, Mehmet ;
Lu, Chris Xiaoxuan ;
Trigoni, Niki ;
Markham, Andrew .
IEEE SENSORS JOURNAL, 2021, 21 (03) :3314-3323
[3]   Situational Graphs for Robot Navigation in Structured Indoor Environments [J].
Bavle, Hriday ;
Luis Sanchez-Lopez, Jose ;
Shaheer, Muhammad ;
Civera, Javier ;
Voos, Holger .
IEEE ROBOTICS AND AUTOMATION LETTERS, 2022, 7 (04) :9107-9114
[4]  
Benser ET, 2015, INT SYMP INERT SENSO, P1
[5]   LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments [J].
Chang, Yun ;
Ebadi, Kamak ;
Denniston, Christopher E. ;
Ginting, Muhammad Fadhil ;
Rosinol, Antoni ;
Reinke, Andrzej ;
Palieri, Matteo ;
Shi, Jingnan ;
Chatterjee, Arghya ;
Morrell, Benjamin ;
Agha-mohammadi, Ali-akbar ;
Carlone, Luca .
IEEE ROBOTICS AND AUTOMATION LETTERS, 2022, 7 (04) :9175-9182
[6]  
[陈先中 Chen Xianzhong], 2020, [煤炭学报, Journal of China Coal Society], V45, P2182
[7]   Monocular Vision Aided Depth Map from RGB Images to Estimate of Localization and Support to Navigation of Mobile Robots [J].
da Silva, Suane Pires Pinheiro ;
Almeida, Jefferson Silva ;
Ohata, Elene Firmeza ;
Rodrigues, Joel J. P. C. ;
de Albuquerque, Victor Hugo C. ;
Reboucas Filho, Pedro Pedrosa .
IEEE SENSORS JOURNAL, 2020, 20 (20) :12040-12048
[8]   Adaptive Tracking Strategy for the Positioning of Millimeter-Wave Radar Security Robots [J].
Dai, Hu ;
Zheng, Rui ;
Ma, Xiaolu ;
Lu, Zibao ;
Sun, Geng ;
Xu, Zhengyou ;
Fan, Chengwei ;
Wu, Min .
IEEE SENSORS JOURNAL, 2024, 24 (13) :21321-21330
[9]   An EKF Based Approach to Radar Inertial Odometry [J].
Doer, Christopher ;
Trommer, Gert F. .
2020 IEEE INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INTEGRATION FOR INTELLIGENT SYSTEMS (MFI), 2020, :152-159
[10]   Research on Calibration and Parameter Compensation of MEMS Inertial Sensors Based on Error Analysis [J].
He Guang-lin ;
Tao Si-qian ;
Shen Qiang ;
Zhou Pian .
2012 FIFTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2012), VOL 1, 2012, :325-329