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

arXiv: https://arxiv.org/abs/2112.12984 · HTML: https://mlsanity.com/paper/2112.12984/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 24 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net](https://mlsanity.com/paper/2012.12395/) (2020)
- [CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2011.04841/) (2020)

## Built on by (most cited first, 4 of 4)

- [DICP: Doppler Iterative Closest Point Algorithm](https://mlsanity.com/paper/2201.11944/) (2022)
- [Learning Moving-Object Tracking with FMCW LiDAR](https://mlsanity.com/paper/2203.00959/) (2022)
- [PAVAS: Physics-Aware Video-to-Audio Synthesis](https://mlsanity.com/paper/2512.08282/) (2025)
- [FMCW-LIO: A Doppler LiDAR-Inertial Odometry](https://mlsanity.com/paper/2609.29374/) (2026)

## Abstract (arXiv)

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.