  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.
