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

Liangsheng Jia, Yinchu Wang, Li Ma, Zhi He, Zixu Li, Yongjie Cui

Abstract read

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Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Liangsheng JiaCollege of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China.

Yinchu WangCollege of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China.

Li MaCollege of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China.

Zhi HeCollege of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China.

Zixu LiCollege of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China.

Yongjie CuiCollege of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China.

Funding

National Natural Science Foundation of China 31971805

6 · The paper itself

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.

Indexed as

Kalman filteringmobile robot in kiwifruit orchardsoutdoor integrated positioningparticle filteringUWB positioning

Identifiers

PMID37688027

DOI10.3390/s23177570

PMCPMC10490773

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.