UNRIO：面向雷达-惯性里程计的不确定性感知速度学习
UNRIO: Uncertainty-Aware Velocity Learning for Radar-Inertial Odometry
AI总结：
UNRIO通过直接处理雷达IQ信号预测体坐标系速度，结合Transformer架构和GRT框架，在多模式下实现不确定性估计与姿态图融合，有效提升横向运动轨迹中的里程计精度。
摘要：
我们提出了UNRIO，一种基于雷达和惯性导航的里程计系统，直接从毫米波雷达的IQ信号估计自身速度，而非处理后的点云数据。现有雷达-惯性里程计方法依赖手工设计的信号处理流程，丢弃原始频谱中的潜在信息并需要仔细调整参数。为此，我们提出了一种基于Transformer的神经网络，基于GRT架构处理完整的4维频谱立方体，以两种模式预测身体坐标系速度：直接线性速度估计和每角度bin的多普勒速度图。网络经过三个阶段训练：几何预训练在LiDAR投影深度上，速度或多普勒微调，以及通过负对数似然损失进行不确定性校准，使其能够同时生成预测和不确定性估计。这些不确定性估计被传播到滑动窗口姿态图中，融合雷达速度因素与IMU预积分测量。我们在IQ1M数据集上训练和评估UNRIO，涵盖多样化的室内环境，包括训练期间未见的前后和横向运动模式。我们的方法在大多数序列中实现了最低的相对姿态误差，特别是在横向运动轨迹中，相较于传统DSP基线表现尤为突出。
英文摘要：
mmWave radars are robust to darkness and occlusions such as dust and smoke, and can directly constrain ego-velocity from a single frame via Doppler measurements, making them attractive sensors for odometry in visually denied conditions. However, almost all existing radar-inertial odometry systems rely on lossy radar point clouds that are highly sparse, generally concentrated in a narrow angular band, and aliased at high speeds. We propose to instead estimate ego-velocity directly from unfiltered mmWave I/Q signals. Taking advantage of a foundation model for 4D radar spectrum, we develop a system that integrates uncertainty-aware velocity predictions with IMU measurements using an uncertainty-weighted sliding-window pose graph to accurately compute odometry even when provided radar data with aliasing or an unfavorable field of view. Evaluated on public benchmark datasets, our system, UNRIO, attains the lowest relative pose error on the majority of sequences across held-out environments, despite differing chirp configurations, motion patterns, and platforms with its strongest gains coming where point clouds fail most: lateral motion in IQ1M and heavily aliased Doppler in ColoRadar.
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