ClaRO: A Cluster-Based Method for Radar Odometry

Abstract

We present ClaRO, an unsupervised, cluster-based odometry pipeline for 4D imaging radar to improve robustness and reduce long-term drift for radar-only odometry.Our core idea is to utilize density-based clustering to improve ego-motion estimation using per-cluster Doppler-residuals and find a local best result to seed a robust least squares in addition to finding static points for pose estimation. The resulting global inlier mask is used to refine the pose via a weighted iterative closest point (ICP) approach that fuses Doppler and radar cross-section (RCS) cues while down-weighting the radar’s weak elevation axis. The method is fully unsupervised and sensor-agnostic. We compare our method on multiple public datasets (View-of-Delft, HeRCULES, and NTU4DRadLM) and show that our approach provides results that match or exceed recent radar-only odometry baselines (Radar4Motion, EFEAR-4D) and pose graph based 4DRadarSLAM. Our cluster-wise approach significantly reduces long-term drift, achieving a 72.0% improvement in mean absolute trajectory error (ATE) and a 73.2% improvement in absolute rotation error (ARE) compared to state-of-the-art radar odometry methods on the HeRCULES dataset. While remaining competitive in relative pose estimation, our method improves global trajectory consistency and demonstrates robustness across different radar sensors and environments on the VoD and NTU4DRadLM benchmarks.