VGC-RIO: A Tightly Integrated Radar-Inertial Odometry With Spatial Weighted Doppler Velocity and Local Geometric Constrained RCS Histograms

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.