Search Results for author: Paul Newman

Found 38 papers, 3 papers with code

VDNA-PR: Using General Dataset Representations for Robust Sequential Visual Place Recognition

no code implementations14 Mar 2024 Benjamin Ramtoula, Daniele De Martini, Matthew Gadd, Paul Newman

Two parallel lines of work on VPR have shown, on one side, that general-purpose off-the-shelf feature representations can provide robustness to domain shifts, and, on the other, that fused information from sequences of images improves performance.

Visual Place Recognition

That's My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation

no code implementations7 Mar 2024 Georgi Pramatarov, Matthew Gadd, Paul Newman, Daniele De Martini

This paper is about 3D pose estimation on LiDAR scans with extremely minimal storage requirements to enable scalable mapping and localisation.

3D Pose Estimation

Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling

no code implementations27 Feb 2024 David S. W. Williams, Matthew Gadd, Paul Newman, Daniele De Martini

This work proposes a semantic segmentation network that produces high-quality uncertainty estimates in a single forward pass.

Segmentation Semantic Segmentation

RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model

no code implementations16 Feb 2024 Jianhao Yuan, Shuyang Sun, Daniel Omeiza, Bo Zhao, Paul Newman, Lars Kunze, Matthew Gadd

Recent advancements in Multi-Modal Large Language models (MLLMs) have shown promising potential in enhancing the explainability as a driving agent by producing control predictions along with natural language explanations.

Autonomous Driving Decision Making +4

Open-RadVLAD: Fast and Robust Radar Place Recognition

1 code implementation27 Jan 2024 Matthew Gadd, Paul Newman

We achieve a median of 91. 52% in Recall@1, outstripping the 69. 55% for the only other open implementation, RaPlace, and at a fraction of its computational cost (relying on fewer integral transforms e. g. Radon, Fourier, and inverse Fourier).

Computational Efficiency Translation

What you see is what you get: Experience ranking with deep neural dataset-to-dataset similarity for topological localisation

1 code implementation20 Oct 2023 Matthew Gadd, Benjamin Ramtoula, Daniele De Martini, Paul Newman

In the case of localisation, important dataset differences impacting performance are modes of appearance change, including weather, lighting, and season.

Visual Navigation

BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR

no code implementations30 Jun 2022 Georgi Pramatarov, Daniele De Martini, Matthew Gadd, Paul Newman

This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching.

Graph Matching Instance Segmentation +2

Depth-SIMS: Semi-Parametric Image and Depth Synthesis

no code implementations7 Mar 2022 Valentina Musat, Daniele De Martini, Matthew Gadd, Paul Newman

In this paper we present a compositing image synthesis method that generates RGB canvases with well aligned segmentation maps and sparse depth maps, coupled with an in-painting network that transforms the RGB canvases into high quality RGB images and the sparse depth maps into pixel-wise dense depth maps.

Depth Completion Image Generation +2

Contrastive Learning for Unsupervised Radar Place Recognition

no code implementations6 Oct 2021 Matthew Gadd, Daniele De Martini, Paul Newman

We learn, in an unsupervised way, an embedding from sequences of radar images that is suitable for solving the place recognition problem with complex radar data.

Contrastive Learning Data Augmentation

The Oxford Road Boundaries Dataset

no code implementations16 Jun 2021 Tarlan Suleymanov, Matthew Gadd, Daniele De Martini, Paul Newman

In this paper we present the Oxford Road Boundaries Dataset, designed for training and testing machine-learning-based road-boundary detection and inference approaches.

Boundary Detection

Unsupervised Place Recognition with Deep Embedding Learning over Radar Videos

no code implementations12 Jun 2021 Matthew Gadd, Daniele De Martini, Paul Newman

We learn, in an unsupervised way, an embedding from sequences of radar images that is suitable for solving place recognition problem using complex radar data.

Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning

no code implementations1 Mar 2021 David Williams, Matthew Gadd, Daniele De Martini, Paul Newman

In this work, we train a network to simultaneously perform segmentation and pixel-wise Out-of-Distribution (OoD) detection, such that the segmentation of unknown regions of scenes can be rejected.

Contrastive Learning Data Augmentation +3

Self-Supervised Localisation between Range Sensors and Overhead Imagery

no code implementations3 Jun 2020 Tim Y. Tang, Daniele De Martini, Shangzhe Wu, Paul Newman

Publicly available satellite imagery can be an ubiquitous, cheap, and powerful tool for vehicle localisation when a prior sensor map is unavailable.

Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision

no code implementations11 May 2020 David Williams, Daniele De Martini, Matthew Gadd, Letizia Marchegiani, Paul Newman

Reliable outdoor deployment of mobile robots requires the robust identification of permissible driving routes in a given environment.

RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar

no code implementations2 Apr 2020 Prannay Kaul, Daniele De Martini, Matthew Gadd, Paul Newman

This paper presents an efficient annotation procedure and an application thereof to end-to-end, rich semantic segmentation of the sensed environment using FMCW scanning radar.

Image Segmentation Segmentation +1

Rainy screens: Collecting rainy datasets, indoors

no code implementations10 Mar 2020 Horia Porav, Valentina-Nicoleta Musat, Tom Bruls, Paul Newman

Acquisition of data with adverse conditions in robotics is a cumbersome task due to the difficulty in guaranteeing proper ground truth and synchronising with desired weather conditions.

Image Reconstruction Segmentation +1

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.


Learning to Correct 3D Reconstructions from Multiple Views

no code implementations22 Jan 2020 Ştefan Săftescu, Paul Newman

We make the observation that features in the feature maps are viewpoint-dependent, and propose a method for transforming features with dynamic filters generated by a multi-layer perceptron from the relative poses between views.

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.

Learning Geometrically Consistent Mesh Corrections

no code implementations8 Sep 2019 Ştefan Săftescu, Paul Newman

In contrast to previous work, we pay attention to the problem of excessive smoothing in corrected meshes.

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

Don't Worry About the Weather: Unsupervised Condition-Dependent Domain Adaptation

no code implementations25 Jul 2019 Horia Porav, Tom Bruls, Paul Newman

Modern models that perform system-critical tasks such as segmentation and localization exhibit good performance and robustness under ideal conditions (i. e. daytime, overcast) but performance degrades quickly and often catastrophically when input conditions change.

Domain Adaptation Segmentation +1

Online Inference and Detection of Curbs in Partially Occluded Scenes with Sparse LIDAR

no code implementations11 Jul 2019 Tarlan Suleymanov, Lars Kunze, Paul Newman

Hence, we believe that our LIDAR-based approach provides an efficient and effective way to detect visible and occluded curbs around the vehicles in challenging driving scenarios.

Autonomous Vehicles Motion Planning

Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation

no code implementations10 Jul 2019 Tom Bruls, Horia Porav, Lars Kunze, Paul Newman

Road markings provide guidance to traffic participants and enforce safe driving behaviour, understanding their semantic meaning is therefore paramount in (automated) driving.

Training Object Detectors With Noisy Data

no code implementations17 May 2019 Simon Chadwick, Paul Newman

The availability of a large quantity of labelled training data is crucial for the training of modern object detectors.

General Classification Object +2

Radar-only ego-motion estimation in difficult settings via graph matching

no code implementations25 Apr 2019 Sarah H. Cen, Paul Newman

Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation.

Graph Matching Motion Estimation

Distant Vehicle Detection Using Radar and Vision

no code implementations30 Jan 2019 Simon Chadwick, Will Maddern, Paul Newman

Given the possible closing speeds between two vehicles, this necessitates the ability to accurately detect distant vehicles.


Imminent Collision Mitigation with Reinforcement Learning and Vision

no code implementations3 Jan 2019 Horia Porav, Paul Newman

This work examines the role of reinforcement learning in reducing the severity of on-road collisions by controlling velocity and steering in situations in which contact is imminent.

reinforcement-learning Reinforcement Learning (RL)

I Can See Clearly Now : Image Restoration via De-Raining

no code implementations3 Jan 2019 Horia Porav, Tom Bruls, Paul Newman

We present a method for improving segmentation tasks on images affected by adherent rain drops and streaks.

Denoising Image Reconstruction +3

The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping

no code implementations3 Dec 2018 Tom Bruls, Horia Porav, Lars Kunze, Paul Newman

Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view.

Autonomous Vehicles Object Tracking +1

Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes

no code implementations11 Oct 2018 Letizia Marchegiani, Paul Newman

This paper is about alerting acoustic event detection and sound source localisation in an urban scenario.

Denoising Event Detection +3

Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer

no code implementations9 Mar 2018 Horia Porav, Will Maddern, Paul Newman

We present a method of improving visual place recognition and metric localisation under very strong appear- ance change.

Visual Place Recognition

Meshed Up: Learnt Error Correction in 3D Reconstructions

no code implementations27 Jan 2018 Michael Tanner, Stefan Saftescu, Alex Bewley, Paul Newman

We train a suitably deep network architecture with two 3D meshes: a high-quality laser reconstruction, and a lower quality stereo image reconstruction.

Image Reconstruction

Geometric Multi-Model Fitting with a Convex Relaxation Algorithm

no code implementations CVPR 2018 Paul Amayo, Pedro Pinies, Lina M. Paz, Paul Newman

Unlike greedy methods --which maximise the number of inliers-- this approach efficiently searches for a soft assignment of points to models by minimising the energy of the overall classification.

Homography Estimation

DENSER Cities: A System for Dense Efficient Reconstructions of Cities

no code implementations13 Apr 2016 Michael Tanner, Pedro Pinies, Lina Maria Paz, Paul Newman

The computational and memory requirements of large dense models can be prohibitive.

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