Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Feng, Huaiqu, Zhao, Guoyang, Liu, Cheng, Wang, Yongwei, Wang, Jun
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W4414838285
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