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The use of infrastructure sensor technology for traffic detection has alreadybeen proven several times. However, extrinsic sensor calibration is still achallenge for the operator. While previous approaches are unable to calibratethe sensors without the use of reference objects in the sensor field of view(FOV), we present an algorithm that is completely detached from externalassistance and runs fully automatically. Our method focuses on thehigh-precision fusion of LiDAR point clouds and is evaluated in simulation aswell as on real measurements. We set the LiDARs in a continuous pendulum motionin order to simulate real-world operation as closely as possible and toincrease the demands on the algorithm. However, it does not receive anyinformation about the initial spatial location and orientation of the LiDARsthroughout the entire measurement period. Experiments in simulation as well aswith real measurements have shown that our algorithm performs a continuouspoint cloud registration of up to four 64-layer LiDARs in real-time. Theaveraged resulting translational error is within a few centimeters and theaveraged error in rotation is below 0.15 degrees.

Real-Time Point Cloud Fusion of Multi-LiDAR Infrastructure Sensor Setups with Unknown Spatial Location and Orientation