Do We Need to Compensate for Motion Distortion and Doppler Effects in Spinning Radar Navigation?

In order to tackle the challenge of unfavorable weather conditions such as rain and snow, radar is being revisited as a parallel sensing modality to vision and lidar. Recent works have made tremendous progress in applying spinning radar to odometry and place recognition. However, these works have so far ignored the impact of motion distortion and Doppler effects on spinning-radar-based navigation, which may be significant in the self-driving car domain where speeds can be high. In this work, we demonstrate the effect of these distortions on radar odometry using the Oxford Radar RobotCar Dataset and metric localization using our own data-taking platform. We revisit a lightweight estimator that can recover the motion between a pair of radar scans while accounting for both effects. Our conclusion is that both motion distortion and the Doppler effect are significant in different aspects of spinning radar navigation, with the former more prominent than the latter. Code for this project can be found at: https://github.com/keenan-burnett/yeti_radar_odometry

[1]  Angela P. Schoellig,et al.  Zeus: A system description of the two‐time winner of the collegiate SAE autodrive competition , 2020, J. Field Robotics.

[2]  Heng Yang,et al.  TEASER: Fast and Certifiable Point Cloud Registration , 2020, IEEE Transactions on Robotics.

[3]  Andrew M. Wallace,et al.  RADIATE: A Radar Dataset for Automotive Perception in Bad Weather , 2020, 2021 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Yasin Almalioglu,et al.  milliEgo: single-chip mmWave radar aided egomotion estimation via deep sensor fusion , 2020, SenSys.

[5]  Sen Wang,et al.  RadarSLAM: Radar based Large-Scale SLAM in All Weathers , 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[6]  Andrew Kramer,et al.  Radar-Inertial Ego-Velocity Estimation for Visually Degraded Environments , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Jinyong Jeong,et al.  MulRan: Multimodal Range Dataset for Urban Place Recognition , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[8]  Yeong Sang Park,et al.  PhaRaO: Direct Radar Odometry using Phase Correlation , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Matthew Gadd,et al.  RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar , 2020, 2020 IEEE Intelligent Vehicles Symposium (IV).

[10]  Matthew Gadd,et al.  Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance , 2020, 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[11]  Ingmar Posner,et al.  Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Matthew Gadd,et al.  Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[13]  P. Newman,et al.  RSL-Net: Localising in Satellite Images From a Radar on the Ground , 2020, IEEE Robotics and Automation Letters.

[14]  P. Newman,et al.  The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset , 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[15]  I. Posner,et al.  Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information , 2019, CoRL.

[16]  Paul Newman,et al.  Fast Radar Motion Estimation with a Learnt Focus of Attention using Weak Supervision , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[17]  Paul Newman,et al.  Radar-only ego-motion estimation in difficult settings via graph matching , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[18]  Paul Newman,et al.  Probably Unknown: Deep Inverse Sensor Modelling Radar , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[19]  Paul Newman,et al.  Precise Ego-Motion Estimation with Millimeter-Wave Radar Under Diverse and Challenging Conditions , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Timothy D. Barfoot,et al.  State Estimation for Robotics , 2017 .

[21]  Tim D. Barfoot,et al.  Full STEAM ahead: Exactly sparse gaussian process regression for batch continuous-time trajectory estimation on SE(3) , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[22]  Paul Timothy Furgale,et al.  Continuous-time batch trajectory estimation using temporal basis functions , 2015, Int. J. Robotics Res..

[23]  Simo Särkkä,et al.  Batch nonlinear continuous-time trajectory estimation as exactly sparse Gaussian process regression , 2014, Autonomous Robots.

[24]  Simo Särkkä,et al.  Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression , 2014, Robotics: Science and Systems.

[25]  Tim D. Barfoot,et al.  RANSAC for motion-distorted 3D visual sensors , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[26]  Klaus C. J. Dietmayer,et al.  Instantaneous ego-motion estimation using Doppler radar , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[27]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[28]  Roland Chapuis,et al.  Localization and Mapping Using Only a Rotating FMCW Radar Sensor , 2013, Sensors.

[29]  Michael Felsberg,et al.  Rolling shutter bundle adjustment , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[31]  Hermann Rohling,et al.  Ordered statistic CFAR technique - an overview , 2011, 2011 12th International Radar Symposium (IRS).

[32]  N. Patrikalakis,et al.  Predicting Millimeter Wave Radar Spectra for Autonomous Navigation , 2010, IEEE Sensors Journal.

[33]  Roland Chapuis,et al.  Radar Scan Matching SLAM Using the Fourier-Mellin Transform , 2009, FSR.

[34]  Michael Bosse,et al.  Map Matching and Data Association for Large-Scale Two-dimensional Laser Scan-based SLAM , 2008, Int. J. Robotics Res..

[35]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[36]  Martin David Adams,et al.  Relative RADAR cross section based feature identification with millimeter wave RADAR for outdoor SLAM , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[37]  K. S. Arun,et al.  Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.