This paper presents a model-free, setting-independent method for online
detection of dynamic objects in 3D lidar data. We explicitly compensate for the
moving-while-scanning operation (motion distortion) of present-day 3D spinning
lidar sensors. Our detection method uses a motion-compensated freespace
querying algorithm and classifies between dynamic (currently moving) and static
(currently stationary) labels at the point level. For a quantitative analysis,
we establish a benchmark with motion-distorted lidar data using CARLA, an
open-source simulator for autonomous driving research. We also provide a
qualitative analysis with real data using a Velodyne HDL-64E in driving
scenarios. Compared to existing 3D lidar methods that are model-free, our
method is unique because of its setting independence and compensation for
pointcloud motion distortion.

本文提出了一种无模型、独立于设置的方法，可以在线检测 3D 激光雷达数据中的动态物体。我们明确地补偿了当前 3D 旋转激光雷达传感器的移动扫描操作（运动畸变）。我们的检测方法使用一种运动补偿的 freespace 查询算法，并在点级别对动态（当前移动）和静态（当前静止）标签进行分类。我们使用 CARLA 建立了基于运动畸变激光雷达数据的基准进行定量分析，CARLA 是用于自动驾驶研究的开源模拟器。我们还使用 Velodyne HDL-64E 在驾驶场景中提供了实际数据的定性分析。与现有的无模型 3D 激光雷达方法相比，我们的方法独特之处在于其设置独立性和对点云运动畸变的补偿。