Doppler velocity-based algorithm for Clustering and Velocity Estimation of moving objects

TL;DR

This paper introduces a real-time Doppler velocity-based clustering and velocity estimation algorithm for FMCW LiDAR, achieving high accuracy in motion detection and velocity estimation of multiple objects in autonomous driving scenarios.

Contribution

The paper presents a novel algorithm that combines Doppler velocity characteristics with clustering and least-squares estimation for accurate, real-time motion detection and velocity measurement of objects.

Findings

Processes over 4.5 million points per second.

Detects motion state with over 99% accuracy.

Estimates object velocities with 0.1 m/s precision.

Abstract

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.