Radar odometry
4 papers with code • 1 benchmarks • 3 datasets
Radar odometry is the task of estimating the trajectory of the radar sensor, e.g. as presented in https://arxiv.org/abs/2105.01457. A well established performance metric was presented by Geiger (2012) - "Are we ready for autonomous driving? the KITTI vision benchmark suite"
Benchmarks
These leaderboards are used to track progress in Radar odometry
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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Oxford Radar RobotCar Dataset
K-Radar
ORU Diverse radar dataset
Most implemented papers
Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar
utiasASRL/hero_radar_odometry
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This paper presents a self-supervised framework for learning to detect robust keypoints for odometry estimation and metric localisation in radar.
CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation.
MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation
Our experimental results, on traversals of the Oxford RobotCar dataset with no GPS data, show that MVP can achieve 53% and 93% navigation success rate using VO and RO, respectively, compared to 7% for a vision-only method.
Successive Pose Estimation and Beam Tracking for mmWave Vehicular Communication Systems
Simulation results demonstrate that the proposed SPEBT scheme is capable of providing precise pose estimation information and accurate beam tracking output, while reducing the proportion of beam training overhead to less than 5% averagely.
