Daniil Lisus;Keenan Burnett;David J. Yoon;Richard Poulton;John Marshall;Timothy D. Barfoot
{"title":"Are Doppler Velocity Measurements Useful for Spinning Radar Odometry?","authors":"Daniil Lisus;Keenan Burnett;David J. Yoon;Richard Poulton;John Marshall;Timothy D. Barfoot","doi":"10.1109/LRA.2024.3505821","DOIUrl":null,"url":null,"abstract":"Spinning, frequency-modulated continuous-wave (FMCW) radars with \n<inline-formula><tex-math>$360 ^{\\circ }$</tex-math></inline-formula>\n coverage have been gaining popularity for autonomous-vehicle navigation. However, unlike ‘fixed’ automotive radar, commercially available spinning radar systems typically do not produce radial velocities due to the lack of repeated measurements in the same direction and the fundamental hardware setup. To make these radial velocities observable, we modified the firmware of a commercial spinning radar to use triangular frequency modulation. In this letter, we develop a novel way to use this modulation to extract radial Doppler velocity measurements from consecutive azimuths of a radar intensity scan, without any data association. We show that these noisy, error-prone measurements contain enough information to provide good ego-velocity estimates, and incorporate these estimates into different modern odometry pipelines. We extensively evaluate the pipelines on over \n<inline-formula><tex-math>$\\text{110 km}$</tex-math></inline-formula>\n of driving data in progressively more geometrically challenging autonomous-driving environments. We show that Doppler velocity measurements improve odometry in well-defined geometric conditions and enable it to continue functioning even in severely geometrically degenerate environments, such as long tunnels.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 1","pages":"224-231"},"PeriodicalIF":4.6000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Robotics and Automation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10766427/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Spinning, frequency-modulated continuous-wave (FMCW) radars with
$360 ^{\circ }$
coverage have been gaining popularity for autonomous-vehicle navigation. However, unlike ‘fixed’ automotive radar, commercially available spinning radar systems typically do not produce radial velocities due to the lack of repeated measurements in the same direction and the fundamental hardware setup. To make these radial velocities observable, we modified the firmware of a commercial spinning radar to use triangular frequency modulation. In this letter, we develop a novel way to use this modulation to extract radial Doppler velocity measurements from consecutive azimuths of a radar intensity scan, without any data association. We show that these noisy, error-prone measurements contain enough information to provide good ego-velocity estimates, and incorporate these estimates into different modern odometry pipelines. We extensively evaluate the pipelines on over
$\text{110 km}$
of driving data in progressively more geometrically challenging autonomous-driving environments. We show that Doppler velocity measurements improve odometry in well-defined geometric conditions and enable it to continue functioning even in severely geometrically degenerate environments, such as long tunnels.
期刊介绍:
The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.