Velocity-Variance-Based Dynamic Object Filtering for 4D Imaging Radar Odometry

Accurate ego-motion estimation is crucial for LiDAR odometry in autonomous navigation. However, in dynamic environments, conventional LiDAR-only SLAM systems often struggle to distinguish between static and dynamic objects, resulting in map degradation and localization drift. To address this, we propose an approach that integrates 4D imaging radar into the LiDAR odometry pipeline by leveraging ego-velocity estimation derived from radar Doppler measurements. Specifically, we introduce a velocity-variance-weighted least squares (WLS) framework that estimates ego-velocity using the variance of per-point velocity as an inverse confidence measure. This method inherently reduces the influence of outliers and dynamic targets without requiring explicit outlier removal. The estimated velocity is used to remove dynamic points from the radar point cloud, making it suitable for integration into LiDARbased odometry frameworks. We evaluate our method on realworld radar datasets collected in dynamic outdoor environments. Experimental results demonstrate that our WLS-based filtering significantly improves LiDAR odometry stability, reducing ghost artifacts and increasing convergence speed. compared to LSQonly methods, our approach achieves lower initial alignment error and stable pose estimation.