@inproceedings{4e4681acad1b42338c70f264942fee73,
title = "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.",
author = "Park, \{Yeong Sang\} and Shin, \{Young Sik\} and Ayoung Kim",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Robotics and Automation, ICRA 2020 ; Conference date: 31-05-2020 Through 31-08-2020",
year = "2020",
month = may,
doi = "10.1109/ICRA40945.2020.9197231",
language = "English",
series = "Proceedings - IEEE International Conference on Robotics and Automation",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2617--2623",
booktitle = "2020 IEEE International Conference on Robotics and Automation, ICRA 2020",
address = "United States",
}