融合多段多普勒速度与雷达截面积的紧耦合定位方法
VGC-RIO: A Tightly Integrated Radar-Inertial Odometry with Spatial Weighted Doppler Velocity and Local Geometric Constrained RCS Histograms
- 基于空间加权的多普勒速度融合策略，适应点云分布不均
- 提出结合局部几何与雷达截面特征的点云描述子，提升注册精度
- 在复杂场景下表现稳定，适合恶劣环境下的自动驾驶定位
近年来，4D雷达-惯性里程计在恶劣条件下的自主定位中展现出巨大潜力。然而，如何有效处理稀疏且噪声严重的雷达测量仍是关键挑战。本文提出一种紧耦合雷达-惯性里程计VGC-RIO，引入空间加权模型以适配非均匀分布的雷达点，并设计了一种新型点描述子直方图，融合局部几何特征与雷达散射截面（RCS）特征，提升困难场景下的点云配准性能。通过在公开及自建数据集上的大量实验验证，结果表明该方法具备高精度与强鲁棒性。
原文摘要 · Abstract (English)
Recent advances in 4D radar-inertial odometry have demonstrated promising potential for autonomous lo calization in adverse conditions. However, effective handling of sparse and noisy radar measurements remains a critical challenge. In this paper, we propose a radar-inertial odometry with a spatial weighting method that adapts to unevenly distributed points and a novel point-description histogram for challenging point registration. To make full use of the Doppler velocity from different spatial sections, we propose a weighting calculation model. To enhance the point cloud registration performance under challenging scenarios, we con struct a novel point histogram descriptor that combines local geometric features and radar cross-section (RCS) features. We have also conducted extensive experiments on both public and self-constructed datasets. The results demonstrate the precision and robustness of the proposed VGC-RIO.
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