Semi-Automated Generation of Realistic Simulation Environments from Geospatial Data for Agricultural Robot Navigation

Sergio Sánchez de la Fuente^*, Luis Prieto-López, Miguel Á González-Santamarta, Vicente Matellán-Olivera, Ángel Manuel Guerrero-Higueras
Grupo de Robótica, Universidad de León, León, Castilla y León, Spain
* Corresponding Author: Sergio Sánchez de la Fuente. Email: email
(This article belongs to the Special Issue: Environment Modeling for Applications of Mobile Robots)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.080739

Received 13 February 2026; Accepted 23 April 2026; Published online 13 May 2026

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Abstract

The development and testing of autonomous agricultural robots requires realistic simulation environments that accurately represent field conditions and terrain features. Traditional manual scenario creation is time-consuming, expensive and limits the diversity of testing conditions. This paper presents an integrated two-stage system for semi-automated generation of realistic 3D simulation scenarios. The first stage transforms publicly available geospatial data into high-fidelity 3D terrain models, supporting 23 discrete levels of detail (LoD), from 0 to 22, and generating simulation-ready models compatible with the Gazebo robotics simulator. The second stage provides a web-based tool that enables users to populate generated terrains with crop elements, configuring crop distributions, row patterns, and field geometries through a map interface. Detailed performance evaluation across multiple LoD levels identifies the optimal balance between visual fidelity and computational efficiency, with levels 19–20 providing sufficient geometric detail for accurate sensor simulation while maintaining real-time performance on standard hardware. The complete system integrates with the Robot Operating System (ROS) 2 and Gazebo, significantly reducing scenario creation time by eliminating the need for manual modelling of terrain and agricultural elements. Validation experiments were conducted using a Summit-XL robotic platform equipped with an additional RealSense depth camera. The results demonstrate the system’s capability for developing and testing autonomous navigation algorithms in the generated scenarios.