PhaRaO: Direct Radar Odometry using Phase Correlation

Park, Yeong Sang; Shin, Young-Sik; Kim, Ayoung

doi:10.1109/icra40945.2020.9197231

Cited by 63 publications

(36 citation statements)

References 13 publications

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“…Radar odometry techniques [4,17,3] exist for 2D imaging radar which outputs 2D radar images at low update rates. They use either feature-based methods or direct methods to estimate the motion between consecutive radar images.…”

Section: Related Workmentioning

confidence: 99%

Continuous-time Radar-inertial Odometry for Automotive Radars

Ng¹,

Choi²,

Tan³

et al. 2022

Preprint

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We present an approach for radar-inertial odometry which uses a continuous-time framework to fuse measurements from multiple automotive radars and an inertial measurement unit (IMU). Adverse weather conditions do not have a significant impact on the operating performance of radar sensors unlike that of camera and LiDAR sensors. Radar's robustness in such conditions and the increasing prevalence of radars on passenger vehicles motivate us to look at the use of radar for ego-motion estimation. A continuous-time trajectory representation is applied not only as a framework to enable heterogeneous and asynchronous multi-sensor fusion, but also, to facilitate efficient optimization by being able to compute poses and their derivatives in closed-form and at any given time along the trajectory. We compare our continuous-time estimates to those from a discrete-time radar-inertial odometry approach and show that our continuous-time method outperforms the discrete-time method. To the best of our knowledge, this is the first time a continuous-time framework has been applied to radar-inertial odometry.

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Section: Related Workmentioning

confidence: 99%

Continuous-time Radar-inertial Odometry for Automotive Radars

Ng¹,

Choi²,

Tan³

et al. 2022

Preprint

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show abstract

“…Aldera et al [4] train a classifier on the principal eigenvector of their graph matching problem in order to predict and correct for failures in radar odometry. Park et al [39] applied the Fourier-Mellin Transform to log-polar images computed from downsampled Cartesian images. Barnes et al [11] demonstrated a fully differentiable, correlation-based radar odometry pipeline.…”

Section: Related Workmentioning

confidence: 99%

“…Previous works in this area have made significant progress towards radar-based odometry [2-4, 9, 11, 14-16, 26, 34, 39] and place recognition [20,22,31,43,46]. However, previous approaches to radar odometry have either relied on handcrafted feature extraction [2-4, 14-16, 26, 34], correlative scan matching [11,39], or a (self-)supervised learning algorithm [9,11] that relies on trajectory groundtruth. Barnes and Posner [9] previously showed that learned features have the potential to outperform hand-crafted features.…”

Section: Introductionmentioning

confidence: 99%

Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning

Burnett

Yoon

Schoellig

et al. 2021

Robotics: Science and Systems XVII

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This paper presents a radar odometry method that combines probabilistic trajectory estimation and deep learned features without needing groundtruth pose information. The feature network is trained unsupervised, using only the on-board radar data. With its theoretical foundation based on a data likelihood objective, our method leverages a deep network for processing rich radar data, and a non-differentiable classic estimator for probabilistic inference. We provide extensive experimental results on both the publicly available Oxford Radar RobotCar Dataset and an additional 100 km of driving collected in an urban setting. Our sliding-window implementation of radar odometry outperforms most hand-crafted methods and approaches the current state of the art without requiring a groundtruth trajectory for training. We also demonstrate the effectiveness of radar odometry under adverse weather conditions. Code for this project can be found at: https://github.com/utiasASRL/hero radar odometry

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“…Frequency-Modulated Continuous-Wave (FMCW) radar is receiving increased attention for exploitation in autonomous applications, including for problems related to SLAM [6][7][8][9] as well as scene understanding tasks such as object detection [10], and segmentation [11]. This increasing popularity is evident in several urban autonomy datasets with a radar focus [12,13].…”

Section: Navigation and Scene Understanding From Radarmentioning

confidence: 99%

On the Road: Route Proposal from Radar Self-Supervised by Fuzzy LiDAR Traversability

Broomé

Gadd

Martini

et al. 2020

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This is motivated by a requirement for robust, autonomy-enabling scene understanding in unknown environments. In the method proposed in this paper, discriminative machine-learning approaches are applied to infer traversability and predict routes from Frequency-Modulated Contunuous-Wave (FMCV) radar frames. Firstly, using geometric features extracted from LiDAR point clouds as inputs to a fuzzy-logic rule set, traversability pseudo-labels are assigned to radar frames from which weak supervision is applied to learn traversability from radar. Secondly, routes through the scanned environment can be predicted after they are learned from the odometry traces arising from traversals demonstrated by the autonomous vehicle (AV). In conjunction, therefore, a model pretrained for traversability prediction is used to enhance the performance of the route proposal architecture. Experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community. Our key finding is that joint learning of traversability and demonstrated routes lends itself best to a model which understands where the vehicle should feasibly drive. We show that the traversability characteristics can be recovered satisfactorily, so that this recovered representation can be used in optimal path planning, and that an end-to-end formulation including both traversability feature extraction and routes learned by expert demonstration recovers smooth, drivable paths that are comprehensive in their coverage of the underlying road network. We conclude that the proposed system will find use in enabling mapless vehicle autonomy in extreme environments.

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PhaRaO: Direct Radar Odometry using Phase Correlation

Cited by 63 publications

References 13 publications

Continuous-time Radar-inertial Odometry for Automotive Radars

Continuous-time Radar-inertial Odometry for Automotive Radars

Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning

On the Road: Route Proposal from Radar Self-Supervised by Fuzzy LiDAR Traversability

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