4 accepted papers
Ground segmentation is an essential preprocessing task for autonomous vehicles with 3D LiDARs. Nevertheless, current methods for ground segmentation fall short of achieving optimal performance, primarily hindered by under-segmentation, over-segmentation, slow-segmentation, and poor adaptability. Thi
Visual SLAM has regained attention due to its ability to provide perceptual capabilities and simulation test data for Embodied AI. However, traditional SLAM methods struggle to meet the demands of high-quality scene reconstruction, and Gaussian SLAM systems, despite their rapid rendering and high-qu
Bird's eye view (BEV) segmentation map is a recent development in autonomous driving that provides effective environmental information, such as drivable areas and lane dividers. Most of the existing methods use cameras and LiDAR as inputs for segmentation and the fusion of different modalities is ac
To avoid unmanned ground vehicles being obstructed by deformed terrain in off-road, effective vehicle mobility analysis is required. However, the computational complexity of existing mobility analysis methods, such as discrete element analysis, poses significant challenges when applied to large-scal