Motion-Coupled Mapping Algorithm for Hybrid Rice Canopy

TL;DR

This paper introduces a motion-coupled mapping algorithm that combines sensor data and vehicle motion information to accurately map hybrid rice canopies, improving autonomous navigation and operational efficiency of agricultural robots.

Contribution

The novel algorithm integrates real-time RGB-D data with kinematic and inertial measurements for precise canopy mapping in complex rice fields.

Findings

Enhanced mapping accuracy in rice fields

Improved Agri-UGV navigation reliability

Effective in both controlled and dynamic environments

Abstract

This paper presents a motion-coupled mapping algorithm for contour mapping of hybrid rice canopies, specifically designed for Agricultural Unmanned Ground Vehicles (Agri-UGV) navigating complex and unknown rice fields. Precise canopy mapping is essential for Agri-UGVs to plan efficient routes and avoid protected zones. The motion control of Agri-UGVs, tasked with impurity removal and other operations, depends heavily on accurate estimation of rice canopy height and structure. To achieve this, the proposed algorithm integrates real-time RGB-D sensor data with kinematic and inertial measurements, enabling efficient mapping and proprioceptive localization. The algorithm produces grid-based elevation maps that reflect the probabilistic distribution of canopy contours, accounting for motion-induced uncertainties. It is implemented on a high-clearance Agri-UGV platform and tested in various environments, including both controlled and dynamic rice field settings. This approach significantly enhances the mapping accuracy and operational reliability of Agri-UGVs, contributing to more efficient autonomous agricultural operations.