UNRIO：雷达-惯性里程计的不确定性感知速度学习
Original title: UNRIO: Uncertainty-Aware Velocity Learning for Radar-Inertial Odometry
Source: arXiv cs.ROPublish time unverified
UNRIO 直接从未滤波毫米波 I/Q 信号估计自身速度，结合不确定性加权滑窗位姿图做雷达-惯性里程计，在烟雾黑暗等视觉失效场景及高混叠数据下于多数基准序列取得最低相对位姿误差。
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arXiv:2604.13584v2 Announce Type: replace Abstract: 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.