Neural Network-Based SLAM/GNSS Fusion Localization Algorithm for Agricultural Robots in Orchard GNSS-Degraded or Denied Environments

Huixiang Zhou, Jingting Wang, Yuqi Chen, Lian Hu, Zihao Li, Fuming Xie, Jie He () and Pei Wang ()
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Huixiang Zhou: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Jingting Wang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Yuqi Chen: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Lian Hu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Zihao Li: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Fuming Xie: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Jie He: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Pei Wang: College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 15, 1-18

Abstract: To address the issue of agricultural robot loss of control caused by GNSS signal degradation or loss in complex agricultural environments such as farmland and orchards, this study proposes a neural network-based SLAM/GNSS fusion localization algorithm aiming to enhance the robot’s localization accuracy and stability in weak or GNSS-denied environments. It achieves multi-sensor observed pose coordinate system unification through coordinate system alignment preprocessing, optimizes SLAM poses via outlier filtering and drift correction, and dynamically adjusts the weights of poses from distinct coordinate systems via a neural network according to the GDOP. Experimental results on the robotic platform demonstrate that, compared to the SLAM algorithm without pose optimization, the proposed SLAM/GNSS fusion localization algorithm reduced the whole process average position deviation by 37%. Compared to the fixed-weight fusion localization algorithm, the proposed SLAM/GNSS fusion localization algorithm achieved a 74% reduction in average position deviation during transitional segments with GNSS signal degradation or recovery. These results validate the superior positioning accuracy and stability of the proposed SLAM/GNSS fusion localization algorithm in weak or GNSS-denied environments. Orchard experimental results demonstrate that, at an average speed of 0.55 m/s, the proposed SLAM/GNSS fusion localization algorithm achieves an overall average position deviation of 0.12 m, with average position deviation of 0.06 m in high GNSS signal quality zones, 0.11 m in transitional sections under signal degradation or recovery, and 0.14 m in fully GNSS-denied environments. These results validate that the proposed SLAM/GNSS fusion localization algorithm maintains high localization accuracy and stability even under conditions of low and highly fluctuating GNSS signal quality, meeting the operational requirements of most agricultural robots.

Keywords: agricultural robots; GNSS-degraded or denied environments; fusion localization; neural networks (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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