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Found 7 papers, 2 papers with code
Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset
We describe the release of reference data towards a challenging long-term localisation and mapping benchmark based on the large-scale Oxford RobotCar Dataset.
Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar
This paper presents a self-supervised framework for learning to detect robust keypoints for odometry estimation and metric localisation in radar.
RSL-Net: Localising in Satellite Images From a Radar on the Ground
This paper is about localising a vehicle in an overhead image using FMCW radar mounted on a ground vehicle.
Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information
This paper presents an end-to-end radar odometry system which delivers robust, real-time pose estimates based on a learned embedding space free of sensing artefacts and distractor objects.
The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset
In this paper we present The Oxford Radar RobotCar Dataset, a new dataset for researching scene understanding using Millimetre-Wave FMCW scanning radar data.
Robotics Signal Processing
Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments
We present a self-supervised approach to ignoring "distractors" in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments.
Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy
We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments.
