论文标题
一个基于激光雷达的实时有能力的3D感知系统，用于在城市域中自动驾驶
A LiDAR-based real-time capable 3D Perception System for Automated Driving in Urban Domains
论文作者
论文摘要
我们提出了一种基于激光痛的实时能力的3D感知系统，用于在城市域中自动驾驶。分层系统设计能够同时且在实时条件下同时建模环境的固定和可移动部分。我们的方法通过创新的内尾增强功能扩展了最新的技术，即使在非平板地面表面以及伸出或突出的元素的情况下，即使是可感知的道路使用者和可驱动的走廊。我们描述了一个运行时效率的PointCloud处理管道，包括自适应地面估计，3D聚类和运动分类阶段。根据管道的输出，固定环境以多功能映射和融合方法表示。可移动元素在对象跟踪系统中表示，能够使用多个参考点来说明观点更改。我们通过明确考虑遮挡和歧义案例进一步增强了跟踪系统。使用TUBS ROAD用户数据集的子集评估我们的系统。我们通过考虑以应用程序驱动的方式来增强共同的性能指标。感知系统显示出令人印象深刻的结果，并能够应对地址方案，同时仍保留实时能力。
We present a LiDAR-based and real-time capable 3D perception system for automated driving in urban domains. The hierarchical system design is able to model stationary and movable parts of the environment simultaneously and under real-time conditions. Our approach extends the state of the art by innovative in-detail enhancements for perceiving road users and drivable corridors even in case of non-flat ground surfaces and overhanging or protruding elements. We describe a runtime-efficient pointcloud processing pipeline, consisting of adaptive ground surface estimation, 3D clustering and motion classification stages. Based on the pipeline's output, the stationary environment is represented in a multi-feature mapping and fusion approach. Movable elements are represented in an object tracking system capable of using multiple reference points to account for viewpoint changes. We further enhance the tracking system by explicit consideration of occlusion and ambiguity cases. Our system is evaluated using a subset of the TUBS Road User Dataset. We enhance common performance metrics by considering application-driven aspects of real-world traffic scenarios. The perception system shows impressive results and is able to cope with the addressed scenarios while still preserving real-time capability.
