用高频率IMU融合低分辨率雷达，实现复杂环境下的精准定位。
FD-RIO: Fast Dense Radar Inertial Odometry
- 用卡尔曼滤波融合高频IMU与稠密雷达数据，提升定位精度。
- 在KITTI数据集上表现媲美顶尖方法，部分序列更优。
- 算法轻量易部署，适合实际移动设备实时运行。
基于雷达的里程计是恶劣光照或天气条件下自我运动估计的常用方案，但扫描雷达存在采样率低、空间分辨率差的问题。本文提出FD-RIO，通过融合噪声大、易漂移但采样频率高的IMU数据与稠密雷达扫描，缓解该问题。据我们所知，这是首个使用卡尔曼滤波融合稠密扫描雷达与IMU的尝试。我们在两个公开数据集上评估方法，采用标准KITTI评价指标，并进行消融实验与运行时分析。基于相位相关的方法结构紧凑、直观，专为真实移动平台硬件部署设计。尽管结构简单，FD-RIO性能与现有先进方法相当，在部分测试序列中表现更优。
原文摘要 · Abstract (English)
Radar-based odometry is a popular solution for ego-motion estimation in conditions where other exteroceptive sensors may degrade, whether due to poor lighting or challenging weather conditions; however, scanning radars have the downside of relatively lower sampling rate and spatial resolution. In this work, we present FD-RIO, a method to alleviate this problem by fusing noisy, drift-prone, but high-frequency IMU data with dense radar scans. To the best of our knowledge, this is the first attempt to fuse dense scanning radar odometry with IMU using a Kalman filter. We evaluate our methods using two publicly available datasets and report accuracies using standard KITTI evaluation metrics, in addition to ablation tests and runtime analysis. Our phase correlation -based approach is compact, intuitive, and is designed to be a practical solution deployable on a realistic hardware setup of a mobile platform. Despite its simplicity, FD-RIO is on par with other state-of-the-art methods and outperforms in some test sequences.
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