Low-cost real-time localisation for agricultural robots in unstructured farm environments

Agricultural robots have demonstrated significant potential in enhancing farm operational efficiency and reducing manual labour. However, unstructured and complex farm environments present challenges to the precise localisation and navigation of robots in real time. Furthermore, the high costs of navigation systems in agricultural robots hinder their widespread adoption in cost-sensitive agricultural sectors. This study compared two localisation methods that use the Error State Kalman Filter (ESKF) to integrate data from wheel odometry, a low-cost inertial measurement unit (IMU), a low-cost real-time kinematic global navigation satellite system (RTK-GNSS) and the LiDAR-Inertial Odometry via Smoothing and Mapping (LIO-SAM) algorithm using a low-cost IMU and RoboSense 16-channel LiDAR sensor. These two methods were tested on unstructured farm environments for the first time in this study. Experiment results show that the ESKF sensor fusion method without a LiDAR sensor could save 36% of the cost compared to the method that used the LIO-SAM algorithm while maintaining high accuracy for farming applications.