Search Results for author: Dan Barnes

Found 8 papers, 2 papers with code

Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset

no code implementations24 Feb 2020 Will Maddern, Geoffrey Pascoe, Matthew Gadd, Dan Barnes, Brian Yeomans, Paul Newman

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

2 code implementations29 Jan 2020 Dan Barnes, Ingmar Posner

This paper presents a self-supervised framework for learning to detect robust keypoints for odometry estimation and metric localisation in radar.

Radar odometry

RSL-Net: Localising in Satellite Images From a Radar on the Ground

no code implementations9 Jan 2020 Tim Y. Tang, Daniele De Martini, Dan Barnes, Paul Newman

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

no code implementations9 Sep 2019 Dan Barnes, Rob Weston, Ingmar Posner

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.

Radar odometry

The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset

3 code implementations3 Sep 2019 Dan Barnes, Matthew Gadd, Paul Murcutt, Paul Newman, Ingmar Posner

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

no code implementations17 Nov 2017 Dan Barnes, Will Maddern, Geoffrey Pascoe, Ingmar Posner

We present a self-supervised approach to ignoring "distractors" in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments.

Monocular Visual Odometry

Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy

no code implementations5 Oct 2016 Dan Barnes, Will Maddern, Ingmar Posner

We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments.

Autonomous Driving Segmentation +2

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