No abstract

Digital twins of real environments are valuable tools for generating realistic synthetic data and performing simulations with artificial intelligence and machine learning models. Creating digital twins of urban, on-road environments have been extensively researched in the light of rising momentum in urban planning and autonomous vehicle systems; yet creating digital twins of rugged, off-road environments such as forests, farms, and mountainous areas is still poorly studied. In this work, we propose a pipeline to produce digital twins of off-road environments with a focus on modeling vegetation and uneven terrain. A point cloud map of the off-road environment is first reconstructed using LiDAR scans paired with scan registration algorithms. Terrain segmentation, vegetation segmentation, and Euclidean clustering are applied to separate point cloud objects into individual entities within the digital twin model. Experimental validation is carried out using LiDAR scans collected from an off-road proving ground at the Center of Advanced Vehicular Systems (CAVS) in Mississippi State University. A prototype system is demonstrated with the Mississippi State University Autonomous Vehicle Simulator (MAVS), and the source code and data are publicly available * . The proposed framework has a wide range of applications including virtual autonomous vehicle testing, synthetic data generation, and training of AI models.