VGC-RIO: A Tightly Integrated Radar-Inertial Odometry With Spatial Weighted Doppler Velocity and Local Geometric Constrained RCS Histograms
Jianguang Xiang, Xiaofeng He, Zizhuo Chen, Lilian Zhang, Xincan Luo, Jun Mao
Abstract
Recent advances in 4D radar-inertial odometry have demonstrated promising potential for autonomous localization under adverse conditions. However, effective handling of sparse and noisy radar measurements remains a critical challenge. In this letter, we propose a novel 4D radar-inertial odometry that fuses inertial pre-integration, radar scan matching and radar Doppler velocity in a tight way. Unlike most radar-inertial odometry that fuses the Doppler velocity with equal weights, we integrate each radar point's Doppler reading with an adaptive method that can adjust the weights according to the non-uniform point distribution. We further design a new point descriptor for point-to-point matching by combining the point cloud's local geometric and RCS (Radar Cross Section) information in a histogram. Extensive experiments conducted on multiple datasets demonstrate its localization accuracy improvement and adaptability under different environments and motion conditions.
BibTeX
@inproceedings{ral2025_vgcrioatightlyin,
title = {VGC-RIO: A Tightly Integrated Radar-Inertial Odometry With Spatial Weighted Doppler Velocity and Local Geometric Constrained RCS Histograms},
author = {Jianguang Xiang and Xiaofeng He and Zizhuo Chen and Lilian Zhang and Xincan Luo and Jun Mao},
booktitle = {RA-L 2025},
year = {2025}
}