Improved Monte Carlo Localization for Agricultural Mobile Robots with the Normal Distributions Transform

Localization is crucial for robots to navigate autonomously in agricultural environments. This paper introduces an improved Adaptive Monte Carlo Localization (AMCL) algorithm integrated with the Normal Distributions Transform (NDT) to address the challenges of navigation in agricultural fields. 2D Light Detection and Ranging (LiDAR) measures distances to surrounding objects using laser light, and captures distance data in a single horizontal plane, making it ideal for detecting obstacles and field features such as trees and crop rows. While conventional AMCL has been studied for indoor environments, there is a lack of research on its application in outdoor agricultural settings, particularly when using 2D LiDAR. The proposed method enhances localization accuracy by applying the NDT after the conventional AMCL estimation, refining the pose estimate through a more detailed alignment of the 2D LiDAR data with the map. Simulations conducted in a palm oil plantation environment demonstrate a 53% reduction in absolute pose error and a 50%reduction in relative position error compared to conventional AMCL. This highlights the potential of the AMCL-NDT approach with 2D LiDAR for cost-effective and scalable deployment in precision agriculture.