PhaRaO: Direct Radar Odometry using Phase Correlation

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 report...

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Bibliographic Details
Main Authors: Park, Yeong Sang, Shin, Young-Sik, Kim, Ayoung
Format: Conference Proceeding
Language:English
Subjects:
Correlation
Estimation
Feature extraction
Image resolution
Radar imaging
Sensors
Online Access:Request full text
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Summary: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.
ISSN:2577-087X
DOI:10.1109/ICRA40945.2020.9197231