解决雷达与惯性传感器时间延迟问题，提升自动驾驶定位精度
Impact of Temporal Delay on Radar-Inertial Odometry
- 在因子图优化中在线校准雷达与IMU的时间偏移
- 无需扫描匹配或目标跟踪，定位误差显著降低
- 适合需要高鲁棒性的自动驾驶环境感知系统
精确的自身运动估计是任何自主系统的关键。传统传感器如摄像头和激光雷达在雾、暴雨或尘土等恶劣环境下性能会下降。车载雷达因其对这些条件的强适应性，成为自身运动估计中的互补传感器或有前景的替代方案。本文提出一种新型雷达-惯性里程计（RIO）系统，融合车载雷达与惯性测量单元。核心贡献是在因子图优化框架中集成在线时间延迟校准，以补偿雷达与IMU测量之间可能存在的时序偏移。通过在真实世界雷达与IMU数据上进行充分实验验证，结果表明：即使不依赖扫描匹配或目标跟踪，引入在线时间校准也能显著降低定位误差，相较于忽略时间同步的系统，凸显了准确时序对齐在雷达传感融合中的关键作用。
原文摘要 · Abstract (English)
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.
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