Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Aug 31;23(17):7570.
doi: 10.3390/s23177570.

Integrated Positioning System of Kiwifruit Orchard Mobile Robot Based on UWB/LiDAR/ODOM

Liangsheng Jia  1 , Yinchu Wang  1 , Li Ma  1 , Zhi He  1 , Zixu Li  1 , Yongjie Cui  1   2   3
Affiliations

Integrated Positioning System of Kiwifruit Orchard Mobile Robot Based on UWB/LiDAR/ODOM

Liangsheng Jia et al. Sensors (Basel). .

Abstract

To address the issue of low positioning accuracy of mobile robots in trellis kiwifruit orchards with weak signal environments, this study investigated an outdoor integrated positioning method based on ultra-wideband (UWB), light detection and ranging (LiDAR), and odometry (ODOM). Firstly, a dynamic error correction strategy using the Kalman filter (KF) was proposed to enhance the dynamic positioning accuracy of UWB. Secondly, the particle filter algorithm (PF) was employed to fuse UWB/ODOM/LiDAR measurements, resulting in an extended Kalman filter (EKF) measurement value. Meanwhile, the odometry value served as the predicted value in the EKF. Finally, the predicted and measured values were fused through the EKF to estimate the robot's pose. Simulation results demonstrated that the UWB/ODOM/LiDAR integrated positioning method achieved a mean lateral error of 0.076 m and a root mean square error (RMSE) of 0.098 m. Field tests revealed that compared to standalone UWB positioning, UWB-based KF positioning, and LiDAR/ODOM integrated positioning methods, the proposed approach improved the positioning accuracy by 64.8%, 13.8%, and 38.3%, respectively. Therefore, the proposed integrated positioning method exhibits promising positioning performance in trellis kiwifruit orchards with potential applicability to other orchard environments.

Keywords: Kalman filtering; UWB positioning; mobile robot in kiwifruit orchards; outdoor integrated positioning; particle filtering.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Diagram of the integrated positioning system.
Figure 2
Figure 2
Gazebo model of the integrated positioning system.
Figure 3
Figure 3
Individual trellis simulation model.
Figure 4
Figure 4
Kiwifruit orchard simulation environment.
Figure 5
Figure 5
Integrated positioning methods. (a) Integrated positioning framework diagram. (b) Synergistic application of PF and EKF.
Figure 6
Figure 6
Flowchart of UWB Kalman filtering.
Figure 7
Figure 7
Simplified diagram of robot’s curved motion.
Figure 8
Figure 8
Outlier detection diagram.
Figure 9
Figure 9
Flowchart of particle filtering.
Figure 10
Figure 10
Flowchart of the EKF fusion process.
Figure 11
Figure 11
Experimental site layout.
Figure 12
Figure 12
The positioning results under different positioning methods. (a) UWB. (b) KFUWB. (c) LiDAR/ODOM. (d) UWB/LiDAR/ODOM.
Figure 13
Figure 13
Comparison of trajectories for different positioning methods. (a) Trajectories. (b) Lateral error.
Figure 14
Figure 14
Comparison of target point localization accuracy among different positioning methods. (a) Positioning results. (b) Positioning error.
Figure 15
Figure 15
Kiwifruit orchard environment field.
Figure 16
Figure 16
Positioning experiment. (a) Positioning experiment scene image. (b) Positioning experiment route diagram.
Figure 17
Figure 17
Positioning trajectories of different positioning methods. (a) UWB. (b) KFUWB. (c) LiDAR/ODOM. (d) UWB/LiDAR/ODOM.
Figure 17
Figure 17
Positioning trajectories of different positioning methods. (a) UWB. (b) KFUWB. (c) LiDAR/ODOM. (d) UWB/LiDAR/ODOM.
Figure 18
Figure 18
Lateral errors of different positioning methods.
See this image and copyright information in PMC

References

    1. Mao W., Liu H., Hao W., Yang F., Liu Z. Development of a Combined Orchard Harvesting Robot Navigation System. Remote Sens. 2022;14:675. doi: 10.3390/rs14030675. - DOI
    1. Long Z., Xiang Y., Lei X., Li Y., Hu Z., Dai X. Integrated Indoor Positioning System of Greenhouse Robot Based on UWB/IMU/ODOM/LIDAR. Sensors. 2022;22:4819. doi: 10.3390/s22134819. - DOI - PMC - PubMed
    1. Radcliffe J., Cox J., Bulanon D.M. Machine vision for orchard navigation. Comput. Ind. 2018;98:165–171. doi: 10.1016/j.compind.2018.03.008. - DOI
    1. Li X.F., Li T., Qiu Q., Fan Z.Q., Sun N. Review on autonomous navigation for orchard mobile robots. J. Chin. Agric. Mech. 2022;43:156–164.
    1. Slaughter D.C., Giles D., Downey D. Autonomous robotic weed control systems: A review. Comput. Electron. Agric. 2008;61:63–78. doi: 10.1016/j.compag.2007.05.008. - DOI

LinkOut - more resources