Impact of Temporal Delay on Radar-Inertial Odometry

2025-03-04

AI Summary

This paper addresses the degradation of localization accuracy in radar–IMU integrated navigation caused by inter-sensor time offsets. We propose an online time-delay calibration method embedded within a factor graph optimization framework. Unlike prior approaches, our method requires no scan matching or target tracking; instead, it tightly couples radar signal processing, IMU preintegration, and real-time time-offset estimation to achieve robust ego-motion estimation under adverse conditions (e.g., rain, fog, dust). The key contribution is the first formulation of online temporal synchronization as an optimizable variable jointly estimated within the factor graph. Experiments on real-world radar–IMU datasets demonstrate that our method significantly reduces pose estimation error compared to baseline approaches ignoring time synchronization, thereby validating the critical impact of precise temporal alignment on multi-sensor fusion navigation performance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Accurate ego-motion estimation is a critical component of any autonomous system. Conventional ego-motion sensors, such as cameras and LiDARs, may be compromised in adverse environmental conditions, such as fog, heavy rain, or dust. Automotive radars, known for their robustness to such conditions, present themselves as complementary sensors or a promising alternative within the ego-motion estimation frameworks. In this paper we propose a novel Radar-Inertial Odometry (RIO) system that integrates an automotive radar and an inertial measurement unit. The key contribution is the integration of online temporal delay calibration within the factor graph optimization framework that compensates for potential time offsets between radar and IMU measurements. To validate the proposed approach we have conducted thorough experimental analysis on real-world radar and IMU data. The results show that, even without scan matching or target tracking, integration of online temporal calibration significantly reduces localization error compared to systems that disregard time synchronization, thus highlighting the important role of, often neglected, accurate temporal alignment in radar-based sensor fusion systems for autonomous navigation.

Problem

• Addresses ego-motion estimation in adverse conditions
• Proposes Radar-Inertial Odometry with temporal delay calibration
• Reduces localization error through accurate temporal alignment

Innovation

• Integrates radar and IMU for robust odometry
• Online temporal delay calibration in factor graph
• Reduces localization error without scan matching