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This paper introduces RadLoc, a comprehensive radar-based global localization pipeline that integrates place recognition and 3-DoF pose estimation into a single, efficient framework. By employing 1D CA-CFAR filtering and a compact spatial descriptor, RadLoc significantly enhances processing speed and robustness across diverse environmental conditions. Experimental results across multiple datasets reveal that RadLoc not only achieves superior performance but also maintains the smallest descriptor size and fastest retrieval time compared to existing methods.
RadLoc achieves unprecedented speed and robustness in radar-based global localization, outperforming state-of-the-art methods while using the smallest descriptor size.
While global localization using spinning radar has gained attention for its robustness to adverse weather and challenging environments, many studies have focused on individual components such as place recognition or pose estimation. In this paper, we take a holistic view of radar sensor-based global localization and present RadLoc, a fast, robust, and lightweight end-to-end pipeline from place recognition to 3-DoF pose estimation. RadLoc accelerates pre-processing using 1D CA-CFAR filtering and leverages the near-range dominance in spinning radar images to design a compact descriptor and an efficient hierarchical coarse-to-fine retrieval strategy. Moreover, coupled with phase correlation-based 3-DoF pose estimation, it forms a versatile global localization module applicable to SLAM and multi-session SLAM systems. Extensive experiments on 15 sequences across 5 datasets demonstrate that RadLoc achieves robust performance while maintaining the smallest descriptor size and fastest retrieval time among state-of-the-art approaches. The supplementary materials are available at https://sparolab.github.io/research/radloc/.
