PhaRaO: Direct Radar Odometry using Phase Correlation

Abstract

Recent studies in radar-based navigation present promising navigation performance using scanning radars. These scanning radar-based odometry methods are mostly feature-based; they detect and match salient features within a radar image. Differing from existing feature-based methods, this paper reports on a method using direct radar odometry, PhaRaO, which infers relative motion from a pair of radar scans via phase correlation. Specifically, we apply the Fourier Mellin transform (FMT) for Cartesian and log-polar radar images to sequentially estimate rotation and translation. In doing so, we decouple rotation and translation estimations in a coarse-to-fine manner to achieve real-time performance. The proposed method is evaluated using large-scale radar data obtained from various environments. The inferred trajectory yields a 2.34% (translation) and 2.93° (rotation) Relative Error (RE) over a 4km path length on average for the odometry estimation.