Research for the Positioning Optimization for Portable Field Terrain Mapping Equipment Based on the Adaptive Unscented Kalman Filter Algorithm - Jiaxing Xie; Zhenbang Yu; Gaotian Liang; Xianbing Fu; Peng Gao; Huili Yin; Daozong Sun; Weixing Wang; Yueju Xue; Jiyuan Shen; Jun Li (Remote Sensing)
Field positioning (FP) is a key technique in the digitalization of agriculture. By integrating sensors and mapping techniques, FP can convey critical information such as soil quality, plant distribution, and topography. Utilizing vehicles for field applications provides precise control and scientific management for agricultural production. Compared to conventional methods, which often struggle with the complexities of field conditions and suffer from insufficient accuracy, this study employs a novel approach using self-developed multi-sensor array hardware as a portable field topographic surveying device. This innovative setup effectively navigates challenging field conditions to collect raw data. Data fusion is carried out using the Unscented Kalman Filter (UKF) algorithm. Building on this, this study combines the good point set and Opposition-based Differential Evolution for a joint improvement of the Slime Mould Algorithm. This is linked with the UKF algorithm to establish loss value feedback, realizing the adaptive parameter adjustment of the UKF algorithm. This reduces the workload of parameter setting and enhances the precision of data fusion. The improved algorithm optimizes parameters with an efficiency increase of 40.43%. Combining professional, mapping-grade total stations for accuracy comparison, the final test results show an absolute error of less than 0.3857 m, achieving decimeter-level precision in field positioning. This provides a new application technology for better implementation of agricultural digitalization.