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Registering the 4D Millimeter Wave Radar Point Clouds Via Generalized Method of Moments

Xingyi Li, Han Zhang, Ziliang Wang, Yukai Yang, Weidong Chen

TL;DR

This paper tackles the challenge of registering sparse and noisy 4D radar point clouds without relying on explicit point correspondences. It introduces GMMR, a correspondence-free registration framework that aligns point clouds by matching generalized moments computed with Gaussian RBF kernels, and proves statistical consistency of the estimator. The method is augmented with CUDA-based acceleration and an ego-motion overlap extraction step to handle partial overlaps common in radar data. Extensive experiments on synthetic and real 4D radar datasets show that GMMR achieves higher accuracy and robustness than baseline methods and can approach LiDAR-based performance in SLAM settings, highlighting its practical impact for radar-based perception systems.

Abstract

4D millimeter wave radars (4D radars) are new emerging sensors that provide point clouds of objects with both position and radial velocity measurements. Compared to LiDARs, they are more affordable and reliable sensors for robots' perception under extreme weather conditions. On the other hand, point cloud registration is an essential perception module that provides robot's pose feedback information in applications such as Simultaneous Localization and Mapping (SLAM). Nevertheless, the 4D radar point clouds are sparse and noisy compared to those of LiDAR, and hence we shall confront great challenges in registering the radar point clouds. To address this issue, we propose a point cloud registration framework for 4D radars based on Generalized Method of Moments. The method does not require explicit point-to-point correspondences between the source and target point clouds, which is difficult to compute for sparse 4D radar point clouds. Moreover, we show the consistency of the proposed method. Experiments on both synthetic and real-world datasets show that our approach achieves higher accuracy and robustness than benchmarks, and the accuracy is even comparable to LiDAR-based frameworks.

Registering the 4D Millimeter Wave Radar Point Clouds Via Generalized Method of Moments

TL;DR

This paper tackles the challenge of registering sparse and noisy 4D radar point clouds without relying on explicit point correspondences. It introduces GMMR, a correspondence-free registration framework that aligns point clouds by matching generalized moments computed with Gaussian RBF kernels, and proves statistical consistency of the estimator. The method is augmented with CUDA-based acceleration and an ego-motion overlap extraction step to handle partial overlaps common in radar data. Extensive experiments on synthetic and real 4D radar datasets show that GMMR achieves higher accuracy and robustness than baseline methods and can approach LiDAR-based performance in SLAM settings, highlighting its practical impact for radar-based perception systems.

Abstract

4D millimeter wave radars (4D radars) are new emerging sensors that provide point clouds of objects with both position and radial velocity measurements. Compared to LiDARs, they are more affordable and reliable sensors for robots' perception under extreme weather conditions. On the other hand, point cloud registration is an essential perception module that provides robot's pose feedback information in applications such as Simultaneous Localization and Mapping (SLAM). Nevertheless, the 4D radar point clouds are sparse and noisy compared to those of LiDAR, and hence we shall confront great challenges in registering the radar point clouds. To address this issue, we propose a point cloud registration framework for 4D radars based on Generalized Method of Moments. The method does not require explicit point-to-point correspondences between the source and target point clouds, which is difficult to compute for sparse 4D radar point clouds. Moreover, we show the consistency of the proposed method. Experiments on both synthetic and real-world datasets show that our approach achieves higher accuracy and robustness than benchmarks, and the accuracy is even comparable to LiDAR-based frameworks.

Paper Structure

This paper contains 20 sections, 2 theorems, 13 equations, 7 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem formulation
  3. GMM-based point cloud registration
  4. GMM-based point cloud registration
  5. The generalized moments and its approximations
  6. Kernel function design
  7. Transformation estimation
  8. Practical implementation details
  9. CUDA acceleration
  10. Ego-motion-based overlap extraction
  11. Experiments
  12. Experimental Setup
  13. Synthetic experiments for Pairwise Registration
  14. Noiseless registration
  15. Noisy registration
  16. ...and 5 more sections

Key Result

Lemma 1

Let $\mathbf\Phi(\mathbf{x}):=[\phi_1(\mathbf{x}),\phi_2(\mathbf{x}),\ldots,\phi_\kappa(\mathbf{x})]^\top$ be a $\kappa$-dimensional vector-valued kernel function where $\phi_k$'s are defined as eq: gaussian rbf. If the centers $\{\mathbf{c}_k\}_{k=1}^\kappa$ do not lie in any hyperplane in $\mathbb

Figures (7)

  • Figure 1: Comparison of 4D radar and LiDAR point clouds. Left: 4D radar point cloud colored based on the Doppler velocity. Right: LiDAR point cloud.
  • Figure 2: The source (red) and target (blue) point clouds for synthetic registration. From left to right: Bunny, Dragon, Buddha, Armadillo.
  • Figure 3: The noisy source (red) and target (blue) point clouds for synthetic registration. From left to right: Noisy Bunny, Noisy Dragon, Noisy Buddha, Noisy Armadillo.
  • Figure 4: Comparison of translation and rotation error w.r.t. the standard deviation of Gaussian noise and outlier ratio.
  • Figure 5: Comparison of translation and rotation error w.r.t. overlap ratio.
  • ...and 2 more figures

Theorems & Definitions (4)

  • Lemma 1
  • proof
  • Theorem 1: Statistical consistency of the estimator
  • proof