  We propose a Doppler velocity-based cluster and velocity estimation algorithm
based on the characteristics of FMCW LiDAR which achieves highly accurate,
single-scan, and real-time motion state detection and velocity estimation. We
prove the continuity of the Doppler velocity on the same object. Based on this
principle, we achieve the distinction between moving objects and stationary
background via region growing clustering algorithm. The obtained stationary
background will be used to estimate the velocity of the FMCW LiDAR by the
least-squares method. Then we estimate the velocity of the moving objects using
the estimated LiDAR velocity and the Doppler velocity of moving objects
obtained by clustering. To ensure real-time processing, we set the appropriate
least-squares parameters. Meanwhile, to verify the effectiveness of the
algorithm, we create the FMCW LiDAR model on the autonomous driving simulation
platform CARLA for spawning data. The results show that our algorithm can
process at least a 4.5million points and estimate the velocity of 150 moving
objects per second under the arithmetic power of the Ryzen 3600x CPU, with a
motion state detection accuracy of over 99% and estimated velocity accuracy of
0.1 m/s.
