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

TL;DR

This paper introduces a novel registration framework for 4D millimeter wave radar point clouds using the Generalized Method of Moments, improving accuracy and robustness over existing methods, and making radar a viable alternative to LiDAR in challenging conditions.

Contribution

It presents a new registration method that does not rely on explicit point correspondences, addressing the challenges of sparse and noisy radar point clouds.

Findings

Achieves higher accuracy than benchmark methods.

Demonstrates robustness in real-world datasets.

Comparable accuracy to LiDAR-based registration.

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.