论文标题
具有未知空间位置和方向的多LADAR基础架构传感器设置的实时点云融合
Real-Time Point Cloud Fusion of Multi-LiDAR Infrastructure Sensor Setups with Unknown Spatial Location and Orientation
论文作者
论文摘要
基础设施传感器技术用于交通检测已经经过多次证明。但是，外在传感器校准仍然是操作员的挑战。虽然先前的方法无法在传感器视野（FOV）中使用参考对象的情况下校准传感器，但我们提出了一种算法，该算法完全与外部帮助并完全自动运行。我们的方法着重于激光点云的高精度融合，并在模拟和实际测量中进行了评估。我们将倍导体设置为连续的摆运动，以便尽可能地模拟现实世界的操作并增加对算法的需求。但是，在整个测量期间，它没有收到有关激光雷达的初始空间位置和方向的任何信息。模拟和实际测量的实验表明，我们的算法实时执行多达四个64层激光雷达的连续点云配准。导致的平均翻译误差在几厘米之内，旋转的平均误差低于0.15度。
The use of infrastructure sensor technology for traffic detection has already been proven several times. However, extrinsic sensor calibration is still a challenge for the operator. While previous approaches are unable to calibrate the sensors without the use of reference objects in the sensor field of view (FOV), we present an algorithm that is completely detached from external assistance and runs fully automatically. Our method focuses on the high-precision fusion of LiDAR point clouds and is evaluated in simulation as well as on real measurements. We set the LiDARs in a continuous pendulum motion in order to simulate real-world operation as closely as possible and to increase the demands on the algorithm. However, it does not receive any information about the initial spatial location and orientation of the LiDARs throughout the entire measurement period. Experiments in simulation as well as with real measurements have shown that our algorithm performs a continuous point cloud registration of up to four 64-layer LiDARs in real-time. The averaged resulting translational error is within a few centimeters and the averaged error in rotation is below 0.15 degrees.
