robots-9

Radar odometry

3260 papers • 126 benchmarks • 313 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"

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Benchmarks

These leaderboards are used to track progress in radar-odometry-9

Trend

Dataset

Best Model

Actions

Oxford Radar RobotCar Dataset

Libraries

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Use these libraries to find radar-odometry-9 models and implementations

Datasets

Oxford Radar RobotCar Dataset

K-Radar

ORU Diverse radar dataset

Subtasks

No subtasks available.

Most implemented papers

Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar

I. Posner, Dan Barnes•Tue Jan 28 2020

A self-supervised framework capable of full mapping and localisation with radar in urban environments, and is sensor agnostic and can be applied to most modalities.

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CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry

A. Lilienthal, Daniel Adolfsson, Martin Magnusson, Anas W. Alhashimi, Henrik Andreasson•Mon May 03 2021

This paper presents an accurate, highly efficient and learning free method for large-scale radar odometry estimation by using a simple filtering technique that keeps the strongest returns, and produces a clean radar data representation and reconstruct surface normals for efficient and accurate scan matching.

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Successive Pose Estimation and Beam Tracking for mmWave Vehicular Communication Systems

Yuanwei Liu, Cen Liu, Guangxu Zhu, F. Liu•Sat Jul 29 2023

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.

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MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation

Michael Milford, Marvin Chancán•Sun Mar 01 2020

This paper proposes a novel motion and visual perception approach, dubbed MVP, that unifies these two sensor modalities for large-scale, target-driven navigation tasks and can learn faster, and is more accurate and robust to both extreme environmental changes and poor GPS data than corresponding vision-only navigation methods.

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Paper Graph

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