Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR

Wen-liang Zhu; Shu-kai Guo

doi:doi:10.11648/j.ijssn.20241201.12

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Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR

Wen-liang Zhu, Shu-kai Guo^*

Published in International Journal of Sensors and Sensor Networks (Volume 12, Issue 1)

Received: 19 February 2024 Accepted: 29 February 2024 Published: 20 March 2024

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Abstract

In recent years, with the rapid growth in technology and demand for industrial robots, Automated Guided Vehicles (AGVs) have found widespread application in industrial workshops and smart logistics, emerging as a global hot research topic. Due to the volatile and complex working environments, the positioning technology of AGV robots is of paramount importance. To address the challenges associated with AGV robot positioning, such as significant accumulated errors in wheel odometer and Inertial Measurement Unit (IMU), susceptibility of Ultra Wide Band (UWB) positioning accuracy to Non Line of Sight (NLOS) errors, as well as the distortion points and drift in point clouds collected by LiDAR during robot motion, a novel positioning method is proposed. Initially, Weighted Extended Kalman Filter (W-EKF) is employed for the loosely coupled integration of wheel odometer and Ultra Wide Band (UWB) data, transformed into W-EKF pose factors. Subsequently, appropriate addition of W-EKF factors is made during the tight coupling of pre-integrated Inertial Measurement Unit (IMU) with 3D-LiDAR to counteract the distortion points, drift, and accumulated errors generated by LiDAR, thereby enhancing positioning accuracy. After experimentation, the algorithm achieved a final positioning error of only 6.9cm, representing an approximately 80% improvement in positioning accuracy compared to the loosely coupled integration of the two sensors.

Published in	International Journal of Sensors and Sensor Networks (Volume 12, Issue 1)
DOI	10.11648/j.ijssn.20241201.12
Page(s)	13-22
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

AGV, Indoor Positioning, W-EKF, Data Fusion

References

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APA Style

Zhu, W., Guo, S. (2024). Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR. International Journal of Sensors and Sensor Networks, 12(1), 13-22. https://doi.org/10.11648/j.ijssn.20241201.12

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ACS Style

Zhu, W.; Guo, S. Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR. Int. J. Sens. Sens. Netw. 2024, 12(1), 13-22. doi: 10.11648/j.ijssn.20241201.12

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AMA Style

Zhu W, Guo S. Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR. Int J Sens Sens Netw. 2024;12(1):13-22. doi: 10.11648/j.ijssn.20241201.12

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@article{10.11648/j.ijssn.20241201.12,
  author = {Wen-liang Zhu and Shu-kai Guo},
  title = {Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR},
  journal = {International Journal of Sensors and Sensor Networks},
  volume = {12},
  number = {1},
  pages = {13-22},
  doi = {10.11648/j.ijssn.20241201.12},
  url = {https://doi.org/10.11648/j.ijssn.20241201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.20241201.12},
  abstract = {In recent years, with the rapid growth in technology and demand for industrial robots, Automated Guided Vehicles (AGVs) have found widespread application in industrial workshops and smart logistics, emerging as a global hot research topic. Due to the volatile and complex working environments, the positioning technology of AGV robots is of paramount importance. To address the challenges associated with AGV robot positioning, such as significant accumulated errors in wheel odometer and Inertial Measurement Unit (IMU), susceptibility of Ultra Wide Band (UWB) positioning accuracy to Non Line of Sight (NLOS) errors, as well as the distortion points and drift in point clouds collected by LiDAR during robot motion, a novel positioning method is proposed. Initially, Weighted Extended Kalman Filter (W-EKF) is employed for the loosely coupled integration of wheel odometer and Ultra Wide Band (UWB) data, transformed into W-EKF pose factors. Subsequently, appropriate addition of W-EKF factors is made during the tight coupling of pre-integrated Inertial Measurement Unit (IMU) with 3D-LiDAR to counteract the distortion points, drift, and accumulated errors generated by LiDAR, thereby enhancing positioning accuracy. After experimentation, the algorithm achieved a final positioning error of only 6.9cm, representing an approximately 80% improvement in positioning accuracy compared to the loosely coupled integration of the two sensors.
},
 year = {2024}
}

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TY  - JOUR
T1  - Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR
AU  - Wen-liang Zhu
AU  - Shu-kai Guo
Y1  - 2024/03/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ijssn.20241201.12
DO  - 10.11648/j.ijssn.20241201.12
T2  - International Journal of Sensors and Sensor Networks
JF  - International Journal of Sensors and Sensor Networks
JO  - International Journal of Sensors and Sensor Networks
SP  - 13
EP  - 22
PB  - Science Publishing Group
SN  - 2329-1788
UR  - https://doi.org/10.11648/j.ijssn.20241201.12
AB  - In recent years, with the rapid growth in technology and demand for industrial robots, Automated Guided Vehicles (AGVs) have found widespread application in industrial workshops and smart logistics, emerging as a global hot research topic. Due to the volatile and complex working environments, the positioning technology of AGV robots is of paramount importance. To address the challenges associated with AGV robot positioning, such as significant accumulated errors in wheel odometer and Inertial Measurement Unit (IMU), susceptibility of Ultra Wide Band (UWB) positioning accuracy to Non Line of Sight (NLOS) errors, as well as the distortion points and drift in point clouds collected by LiDAR during robot motion, a novel positioning method is proposed. Initially, Weighted Extended Kalman Filter (W-EKF) is employed for the loosely coupled integration of wheel odometer and Ultra Wide Band (UWB) data, transformed into W-EKF pose factors. Subsequently, appropriate addition of W-EKF factors is made during the tight coupling of pre-integrated Inertial Measurement Unit (IMU) with 3D-LiDAR to counteract the distortion points, drift, and accumulated errors generated by LiDAR, thereby enhancing positioning accuracy. After experimentation, the algorithm achieved a final positioning error of only 6.9cm, representing an approximately 80% improvement in positioning accuracy compared to the loosely coupled integration of the two sensors.

VL  - 12
IS  - 1
ER  -

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Author Information

Wen-liang Zhu

School of Mechanical Engineering, Jiangsu Ocean University, Lianyungang, China
Shu-kai Guo

School of Mechanical Engineering, Jiangsu Ocean University, Lianyungang, China

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APA Style

Zhu, W., Guo, S. (2024). Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR. International Journal of Sensors and Sensor Networks, 12(1), 13-22. https://doi.org/10.11648/j.ijssn.20241201.12

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ACS Style

Zhu, W.; Guo, S. Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR. Int. J. Sens. Sens. Netw. 2024, 12(1), 13-22. doi: 10.11648/j.ijssn.20241201.12

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AMA Style

Zhu W, Guo S. Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR. Int J Sens Sens Netw. 2024;12(1):13-22. doi: 10.11648/j.ijssn.20241201.12

Copy | Download

@article{10.11648/j.ijssn.20241201.12,
  author = {Wen-liang Zhu and Shu-kai Guo},
  title = {Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR},
  journal = {International Journal of Sensors and Sensor Networks},
  volume = {12},
  number = {1},
  pages = {13-22},
  doi = {10.11648/j.ijssn.20241201.12},
  url = {https://doi.org/10.11648/j.ijssn.20241201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.20241201.12},
  abstract = {In recent years, with the rapid growth in technology and demand for industrial robots, Automated Guided Vehicles (AGVs) have found widespread application in industrial workshops and smart logistics, emerging as a global hot research topic. Due to the volatile and complex working environments, the positioning technology of AGV robots is of paramount importance. To address the challenges associated with AGV robot positioning, such as significant accumulated errors in wheel odometer and Inertial Measurement Unit (IMU), susceptibility of Ultra Wide Band (UWB) positioning accuracy to Non Line of Sight (NLOS) errors, as well as the distortion points and drift in point clouds collected by LiDAR during robot motion, a novel positioning method is proposed. Initially, Weighted Extended Kalman Filter (W-EKF) is employed for the loosely coupled integration of wheel odometer and Ultra Wide Band (UWB) data, transformed into W-EKF pose factors. Subsequently, appropriate addition of W-EKF factors is made during the tight coupling of pre-integrated Inertial Measurement Unit (IMU) with 3D-LiDAR to counteract the distortion points, drift, and accumulated errors generated by LiDAR, thereby enhancing positioning accuracy. After experimentation, the algorithm achieved a final positioning error of only 6.9cm, representing an approximately 80% improvement in positioning accuracy compared to the loosely coupled integration of the two sensors.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR
AU  - Wen-liang Zhu
AU  - Shu-kai Guo
Y1  - 2024/03/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ijssn.20241201.12
DO  - 10.11648/j.ijssn.20241201.12
T2  - International Journal of Sensors and Sensor Networks
JF  - International Journal of Sensors and Sensor Networks
JO  - International Journal of Sensors and Sensor Networks
SP  - 13
EP  - 22
PB  - Science Publishing Group
SN  - 2329-1788
UR  - https://doi.org/10.11648/j.ijssn.20241201.12
AB  - In recent years, with the rapid growth in technology and demand for industrial robots, Automated Guided Vehicles (AGVs) have found widespread application in industrial workshops and smart logistics, emerging as a global hot research topic. Due to the volatile and complex working environments, the positioning technology of AGV robots is of paramount importance. To address the challenges associated with AGV robot positioning, such as significant accumulated errors in wheel odometer and Inertial Measurement Unit (IMU), susceptibility of Ultra Wide Band (UWB) positioning accuracy to Non Line of Sight (NLOS) errors, as well as the distortion points and drift in point clouds collected by LiDAR during robot motion, a novel positioning method is proposed. Initially, Weighted Extended Kalman Filter (W-EKF) is employed for the loosely coupled integration of wheel odometer and Ultra Wide Band (UWB) data, transformed into W-EKF pose factors. Subsequently, appropriate addition of W-EKF factors is made during the tight coupling of pre-integrated Inertial Measurement Unit (IMU) with 3D-LiDAR to counteract the distortion points, drift, and accumulated errors generated by LiDAR, thereby enhancing positioning accuracy. After experimentation, the algorithm achieved a final positioning error of only 6.9cm, representing an approximately 80% improvement in positioning accuracy compared to the loosely coupled integration of the two sensors.

VL  - 12
IS  - 1
ER  -

Copy | Download