no code implementations • 14 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.
no code implementations • 7 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.
no code implementations • 27 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.
no code implementations • 27 Feb 2024 • David S. W. Williams, Daniele De Martini, Matthew Gadd, Paul Newman
Knowing when a trained segmentation model is encountering data that is different to its training data is important.
no code implementations • 16 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.
1 code implementation • 27 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).
1 code implementation • 20 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.
no code implementations • 4 Oct 2023 • Jaewon La, Jaime Phadke, Matt Hutton, Marius Schwinning, Gabriele De Canio, Florian Renk, Lars Kunze, Matthew Gadd
We train a CycleGAN model to synthesise LROC from Planet and Asteroid Natural Scene Generation Utility (PANGU) images.
1 code implementation • 7 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.
no code implementations • CVPR 2023 • Benjamin Ramtoula, Matthew Gadd, Paul Newman, Daniele De Martini
For this, we propose representing images - and by extension datasets - using Distributions of Neuron Activations (DNAs).
no code implementations • 30 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.
no code implementations • 7 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.
no code implementations • 6 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.
no code implementations • 16 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.
no code implementations • 12 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.
no code implementations • 1 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.
no code implementations • 11 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.
no code implementations • 2 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.
no code implementations • 24 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.
3 code implementations • 3 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