A new covariance intersection based integrated SLAM framework for 3D outdoor agricultural applications

Abstract

This letter introduces a novel integrated framework for simultaneous localization and mapping (SLAM) tailored for general agricultural applications. The framework combines a cutting-edge SLAM method, LIO-SAM, with covariance intersection for sensor fusion. Agricultural robots often operate in unstructured environments with sparse feature points and encounter repeated similar information, such as trees. Therefore, a fusion framework based on 3D SLAM augmented with additional information, such as feature-independent GPS data, becomes essential. This study proposes an integrated SLAM framework by introducing a convergence strategy based on covariance analysis, incorporating a state-of-the-art 3D SLAM technique. The convergence methods, namely "dynamic weight assignment" and "winner takes all", are presented alternatively, tailored to seamlessly integrate with the proposed framework. Evaluations using a public dataset and an experiment demonstrate the effectiveness of the approach through numerical analysis and visual representation. The results illustrate that this method surpasses conventional approaches in accurately estimating the robot's position. In the future, this research will focus on automating crop cultivation and harvesting by integrating the proposed system with robot arm control. This study proposes an integrated simultaneous localization and mapping (SLAM) framework by introducing a convergence strategy based on covariance analysis, incorporating a state-of-the-art 3D SLAM technique. The convergence methods, namely "dynamic weight assignment" and "winner takes all", are presented alternatively, tailored to seamlessly integrate with this proposed framework. The experimental results illustrate that this method surpasses conventional approaches in accurately estimating the robot's position. image