一般化モーメント法による4Dミリ波レーダー点群の位置合わせ
Registering the 4D Millimeter Wave Radar Point Clouds Via Generalized Method of Moments
4Dミリ波レーダーの疎でノイズの多い点群を、点対応を必要としない一般化モーメント法で高精度に位置合わせするフレームワークを提案した。
詳しい要約
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著者: Xingyi Li, Han Zhang, Ziliang Wang, Yukai Yang, Weidong Chen
分類: cs.RO, cs.CV
原文アブストラクト
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.
