# DICP: Doppler Iterative Closest Point Algorithm

arXiv: https://arxiv.org/abs/2201.11944 · HTML: https://mlsanity.com/paper/2201.11944/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [Learning to Localize Using a LiDAR Intensity Map](https://mlsanity.com/paper/2012.10902/) (2020)
- [Measuring robustness of Visual SLAM](https://mlsanity.com/paper/1910.04755/) (2019)
- [Doppler velocity-based algorithm for Clustering and Velocity Estimation of moving objects](https://mlsanity.com/paper/2112.12984/) (2021)

## Built on by (most cited first, 8 of 8)

- [Learning Moving-Object Tracking with FMCW LiDAR](https://mlsanity.com/paper/2203.00959/) (2022)
- [FMCW-LIO: A Doppler LiDAR-Inertial Odometry](https://mlsanity.com/paper/2609.29374/) (2026)
- [POD: Predictive Object Detection with Single-Frame FMCW LiDAR Point Cloud](https://mlsanity.com/paper/2504.05649/) (2025)
- [Registration of 3D Point Sets Using Exponential-based Similarity Matrix](https://mlsanity.com/paper/2505.04540/) (2025)
- [Registering the 4D Millimeter Wave Radar Point Clouds Via Generalized Method of Moments](https://mlsanity.com/paper/2508.02187/) (2025)
- [Free-Init: Scan-Free, Motion-Free, and Correspondence-Free Initialization for Doppler LiDAR-Inertial Systems](https://mlsanity.com/paper/2609.29375/) (2026)
- [AevaScenes: An FMCW LiDAR Dataset and Benchmark for Long-Range Perception](https://mlsanity.com/paper/2609.33230/) (2026)
- [DiFF: Doppler-informed Flow Matching for Human Motion Flow](https://mlsanity.com/paper/2609.39098/) (2026)

## Abstract (arXiv)

In this paper, we present a novel algorithm for point cloud registration for range sensors capable of measuring per-return instantaneous radial velocity: Doppler ICP. Existing variants of ICP that solely rely on geometry or other features generally fail to estimate the motion of the sensor correctly in scenarios that have non-distinctive features and/or repetitive geometric structures such as hallways, tunnels, highways, and bridges. We propose a new Doppler velocity objective function that exploits the compatibility of each point's Doppler measurement and the sensor's current motion estimate. We jointly optimize the Doppler velocity objective function and the geometric objective function which sufficiently constrains the point cloud alignment problem even in feature-denied environments. Furthermore, the correspondence matches used for the alignment are improved by pruning away the points from dynamic targets which generally degrade the ICP solution. We evaluate our method on data collected from real sensors and from simulation. Our results show that with the added Doppler velocity residual terms, our method achieves a significant improvement in registration accuracy along with faster convergence, on average, when compared to classical point-to-plane ICP that solely relies on geometric residuals.