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

TL;DR

This paper introduces VGC-RIO, a radar-inertial odometry method that improves localization accuracy and robustness in adverse conditions by using spatial weighting and a novel RCS histogram descriptor for better point registration.

Contribution

It presents a new spatial weighting approach and a RCS-based histogram descriptor to enhance radar point registration and odometry performance in challenging environments.

Findings

Demonstrates high precision in localization

Shows robustness against sparse and noisy radar data

Performs well on public and self-constructed datasets

Abstract

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.