Dynamic Object Localization Based on Radio Frequency Identification and Laser Information

Ran LIU; Gaoli LIANG; Heng WANG; Yulu FU; Jing HE; Hua ZHANG

doi:10.11999/JEIT171088

Volume 40 Issue 11

Oct. 2018

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(11): 2590-2597

Ran LIU, Gaoli LIANG, Heng WANG, Yulu FU, Jing HE, Hua ZHANG. Dynamic Object Localization Based on Radio Frequency Identification and Laser Information[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2590-2597. doi: 10.11999/JEIT171088

Citation:

Ran LIU, Gaoli LIANG, Heng WANG, Yulu FU, Jing HE, Hua ZHANG. Dynamic Object Localization Based on Radio Frequency Identification and Laser Information[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2590-2597. doi: 10.11999/JEIT171088

Citation:

PDF( 2440 KB)

Dynamic Object Localization Based on Radio Frequency Identification and Laser Information

doi: 10.11999/JEIT171088

School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China

Funds: The National Natural Science Foundation of China (61601381, 61701421, 61471306), The National Nuclear Energy Development Program of China ([2016]1295)

Received Date: 2017-11-20
Rev Recd Date: 2018-08-21

Available Online: 2018-08-28

Publish Date: 2018-11-01

Abstract

Abstract

Recent researches show great interests in localizing dynamic objects through cost-effective technologies. Laser or visual-based approaches have to solve the singularity and occlusion problem from the environment. Radio Frequency IDentification (RFID) is used as a preferred technology to address these issues, due to the unique identification and the communication without line of sight. In this paper, an innovative method is proposed to localize precisely a dynamic object equipped with an RFID tag by fusing laser information RFID information. A particle filter is used to fuse RFID signal strength, phase information, and laser ranging data. Particularly, a pre-trained signal strength-based model is used to incorporate the signal strength information. Then, the laser ranging data is divided into different clusters and the velocities of these clusters are compared with the RFID phase velocity. Matching results of both velocities are used to confine the locations of the particles during the update stage of the particle filtering. The proposed approach is verified by several experiments on a SCITOS service robot and results show that the proposed approach provides better localization accuracy when compared with laser-based approach and the signal strength-based approach.
- RSSI signal strength model,
- Radio Frequency IDentification (RFID) phase,
- Laser clustering,
- Velocity matching,
- Particle filtering

FullText(HTML)

References(18)

References

WANT R. An introduction to RFID technology[J]. IEEE Pervasive Computing, 2006, 5(1): 25–33 doi: 10.1109/MPRV.2006.2

YANG Zhixin, ZHANG Pengbo, and CHEN Lei. RFID-enabled indoor positioning method for a real-time manufacturing execution system using OS-ELM[J]. Neurocomputing, 2016, 174(PA): 121–133.

PRINSLOO J and MALEKIAN R. Accurate vehicle location system using RFID, an Internet of Things approach[J]. Sensors, 2016, 16(6): 1–24 doi: 10.3390/s16060825

HAHNEL D, BURGARD W, FOX D, et al. Mapping and localization with RFID technology[C]. IEEE International Conference on Robotics and Automation, Brisbane, Australia, 2004: 1015–1020.

VORST P and ZELL A. Semi-autonomous learning of an RFID sensor model for mobile robot self-localization[C]. European Robotics Symposium, Prague, Czech Republic, 2008: 273–282.

JOHO D, PlLAGEMANN C, and BURGARD W. Modeling RFID signal strength and tag detection for localization and mapping[C]. IEEE International Conference on Robotics and Automation, Kobe, Japan, 2009: 3160–3165.

LIONEL M N, LIU Y H, YIU C L, et al. LANDMARC: Indoor location sensing using active RFID[J]. ACM Wireless Networks, 2004, 10(6): 701–710 doi: 10.1023/B:WINE.0000044029.06344

YANG Lei, GUO Yi, LIU Tianci, et al. Perceiving the slightest tag motion beyond localization[J]. IEEE Transactions on Mobile Computing, 2015, 14(11): 2363–2375 doi: 10.1109/TMC.2015.2393859

YANG Lei, CHEN Yekui, LI Xiangyang, et al. Tagoram: Real-time tracking of mobile RFID tags to high precision using COTS devices[C]. Mobile Computing and Networking, New York, USA, 2014: 237–248.

LIU Ran, HUSKIC G, and ZELL A. Dynamic objects tracking with a mobile robot using passive UHF RFID tags[C]. IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, USA, 2014: 4247–4252.

LIU Ran, HUSKIC G, and ZELL A. On tracking dynamic objects with long range passive UHF RFID using a mobile robot[J]. International Journal of Distributed Sensor Networks, 2015, 2015(P1): 1–12 doi: 10.1155/2015/781380

LIU Ran, YUEN C, DO T N, et al. Indoor positioning using similarity-based sequence and dead reckoning without training[C]. IEEE International Workshop on Signal Processing Advances in Wireless Communications, Sapporo, Japan, 2017: 1–5.

李军怀, 贾金朋, 王怀军, 等. 基于信号强度差的RFID室内定位[J]. 计算机科学, 2015, 42(11): 154–157 doi: 10.11896/j.issn.1002-137X.2015.11.032

LI Junhuai, JIA Jinpeng, WANG Huaijun, et al. Indoor positioning of RFID based on signal strength difference[J]. Computer Science, 2015, 42(11): 154–157 doi: 10.11896/j.issn.1002-137X.2015.11.032

黄保虎, 刘冉, 张华, 等. 基于不同重采样算法的RFID指纹定位[J]. 计算机应用, 2013, 33(2): 595–599 doi: 10.3724/SP.J.1087.2013.00595

HUANG Baohu, LIU Ran, ZHANG Hua, et al. RFID fingerprinting based on different resampling algorithms[J].Computer Application, 2013, 33(2): 595–599 doi: 10.3724/SP.J.1087.2013.00595

李珣, 刘丽, 洪良, 等. 移动机器人室内无源RFID定位方法及实现[J]. 计算机工程与应用, 2017, 53(16): 230–236 doi: 10.3778/j.issn.1002-8331.1608-0434

LI Xun, LIU Li, HONG Liang, et al. Indoor passive RFID location method for mobile robot and its implementation[J]. Computer Engineering and Application, 2017, 53(16): 230–236 doi: 10.3778/j.issn.1002-8331.1608-0434

黄如林, 梁华为, 陈佳佳, 等. 基于激光雷达的无人驾驶汽车动态障碍物检测、跟踪与识别方法[J]. 机器人, 2016, 38(4): 437–443 doi: 10.13973/j.cnki.robot.2016.0437

HUANG Rulin, LIANG Huawei, CHEN Jiajia, et al. Method of tracking and recognition of laser radar unmanned vehicle dynamic obstacle detection[J]. Robot, 2016, 38(4): 437–443 doi: 10.13973/j.cnki.robot.2016.0437

李昀泽. 基于激光雷达的室内机器人SLAM研究[D]. [硕士论文], 华南理工大学, 2016.

LI Yunze. Research on indoor robot SLAM based on laser radar[D]. [Master dissereation], South China University of Technology, 2016.

PRZYBYLA M. Detection and tracking of 2D geometric obstacles from LRF data[C]. IEEE International Workshop on Robot Motion and Control, Bukowy Dworek, Poland, 2017: 135–141.

Relative Articles

Supplements(0)

Cited By

Proportional views