Search Results for author: Matthew Gadd

Found 20 papers, 4 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

SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention

1 code implementation7 Aug 2023 Efimia Panagiotaki, Daniele De Martini, Georgi Pramatarov, Matthew Gadd, Lars Kunze

This paper proposes a Graph Neural Network(GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates.

Graph Attention Inductive Bias +1

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

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

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.


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

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