FAR-RIO: A Fast and Robust Radar-Inertial Odometry With Isotropic Uncertainty Model and Dual-Observation Update Pipeline

Abstract

Due to the ability to provide point clouds and Doppler velocity, as well as the adaptability in harsh weather conditions, 4D Radar has emerged as a new option for Simultaneous Localization and Mapping (SLAM). However, there is limited research on both robustness and computational efficiency, which are essential requirements for deploying 4D Radar as a specialized sensor in harsh weather. This paper proposes a fast and robust Radar-Inertial-Odometry (RIO) approach, named FAR-RIO. Specifically, we propose a dynamic point filtering based on full covariance propagation, along with an isotropic uncertainty model for accurate registration. In particular, leveraging the dual-observation capability of 4D radar, we design a novel dual-observation update pipeline for the iterated error-state Kalman filter (IESKF), coupled with a corresponding keyframe selection strategy, significantly reducing computational load while minimizing accuracy loss. Our method approaches state-of-the-art performance on various sensors and runs significantly faster than existing methods.