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Dr-PoGO is presented, a SLAM method using 2D spinning radar that directly registers radar scans for odometry and loop closure, bypassing explicit feature extraction. To initialize loop closures from the RaPlace place recognition algorithm, a coarse-to-fine registration using visual features provides an initial transformation for direct refinement. The resulting pose-graph optimization achieves state-of-the-art performance on 300km of real-world automotive data.
Radar SLAM can now achieve state-of-the-art performance via direct scan registration, eliminating the need for hand-engineered feature extraction and enabling robust localization in adverse weather.
This paper introduces Dr-PoGO, a method for Simultaneous Localization And Mapping (SLAM) using a 2D spinning radar. Unlike cameras or lidars that require line-of-sight, millimetre-wave radars can `see'through dust, falling snow, rain, etc. Accordingly, it is a great modality for robust perception regardless of the weather conditions. While most existing radar-based SLAM methods rely on the extraction of point clouds or features to perform ego-motion estimation, Dr-PoGO leverages direct registration techniques for odometry (DRO) and loop-closure registration. An off-the-shelf radar-focused place recognition algorithm, RaPlace, provides loop-closure candidates. As RaPlace does not provide relative transformations, Dr-PoGO introduces a coarse-to-fine registration that uses visual features and descriptors to obtain an initial guess for the direct transformation refinement. The global trajectory is optimized in a pose-graph optimization. Dr-PoGO demonstrates state-of-the-art performance over 300km of data in various real-world automotive environments. Our implementation is publicly available: https://github.com/utiasASRL/dr_pogo.
