发表机构
University of the Bundeswehr Munich（慕尼黑联邦国防军大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一个多车辆数据集，提供相机、LiDAR和雷达数据及扫描3D模型，结合RTK-GNSS位姿参考，支持自定义自动标注和测量效应评估。
AI 中文摘要
数据集是感知算法开发中的关键要素。它们将传感器测量数据与标注的参考信息相关联，并允许推断传感器和物体的特性。在自动驾驶中，参考数据通常包括语义图像分割、逐点关联或边界框标注。然而，本工作提出的数据集旨在更深入地评估测量原理，并提供所有车辆的扫描3D模型，以及通过RTK-GNSS获得的位姿和连续运动学参考。结合起来，传感器车辆完整动态周围环境的状态在任何时间点都是已知的。后续的参考格式可以轻松地以用户定义的粒度计算。该数据集包含七个目标车辆的单目标和多目标记录。特别是，由于目标车辆形状的法线已知，可以评估遮挡和反射等测量效应。我们描述了该数据集，讨论了其开发的技术背景，并简要展示了示例性评估。
英文摘要
Datasets are a crucial element in the development of perception algorithms. They relate sensor measurement data to annotated reference information and allow for the deduction of sensor and object characteristics. In autonomous driving, the reference data commonly consist of semantic image segmentation, point-wise associations, or bounding box annotations. The dataset proposed in this work, however, aims to dig deeper into the evaluation of measurement principles and provides scanned 3D models of all vehicles together with a pose and continuous kinematics reference obtained by RTK-GNSS. Combined, the state of the complete dynamic surrounding of the sensor vehicle is known for any point in time. Subsequent reference formats can be easily computed in user-defined granularity. This dataset involves single-object and multi-object recordings with seven target vehicles. In particular, measurement effects such as occlusion, as well as reflections, can be evaluated, as the normals of the shape of the target vehicles are known. We describe the dataset, discuss the technical background of its development, and briefly present exemplary evaluations.
CommentsPaper accompanying the dataset "7V-Scanario"
Journal ref2025 IEEE Sensor Data Fusion: Trends, Solutions, Applications (SDF)
DOI：10.1109/SDF67080.2025.11331266
