Search Results for author: Matthew Gadd

Found 10 papers, 1 papers with code

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.

Association Graph Matching +3

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 +1

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 +2

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 Semantic Segmentation

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

2 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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