1 code implementation • 13 Apr 2018 • Mirto Musci, Daniele De Martini, Nicola Blago, Tullio Facchinetti, Marco Piastra
This information can be used to trigger the necessary assistance in case of injury.
no code implementations • 9 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.
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 • 3 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.
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 • 17 Dec 2020 • Christian Schroeder de Witt, Catherine Tong, Valentina Zantedeschi, Daniele De Martini, Freddie Kalaitzis, Matthew Chantry, Duncan Watson-Parris, Piotr Bilinski
Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world.
no code implementations • 25 Feb 2021 • Luigi Campanaro, Siddhant Gangapurwala, Daniele De Martini, Wolfgang Merkt, Ioannis Havoutis
Our results on a locomotion task using a single-leg hopper demonstrate that explicitly using the CPG as the Actor rather than as part of the environment results in a significant increase in the reward gained over time (6x more) compared with previous approaches.
Robotics
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 • 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 • 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 • 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 • 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 • 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 • 31 Jul 2022 • Zhen Meng, Changyang She, Guodong Zhao, Daniele De Martini
This work proposes a sampling, communication and prediction co-design framework to minimize the communication load subject to a constraint on tracking the Mean Squared Error (MSE) between a real-world device and its digital model in the metaverse.
no code implementations • 7 Feb 2023 • Pawit Kochakarn, Daniele De Martini, Daniel Omeiza, Lars Kunze
This work explores scene graphs as a distilled representation of high-level information for autonomous driving, applied to future driver-action prediction.
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 • 25 Apr 2023 • Luigi Campanaro, Daniele De Martini, Siddhant Gangapurwala, Wolfgang Merkt, Ioannis Havoutis
This paper proposes a simple strategy for sim-to-real in Deep-Reinforcement Learning (DRL) -- called Roll-Drop -- that uses dropout during simulation to account for observation noise during deployment without explicitly modelling its distribution for each state.
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 • 8 Aug 2023 • Efimia Panagiotaki, Daniele De Martini, Lars Kunze
In this work, we propose a methodology for investigating the use of semantic attention to enhance the explainability of Graph Neural Network (GNN)-based models.
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 • 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 • 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 • 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.
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.
1 code implementation • 4 Nov 2021 • Vít Růžička, Anna Vaughan, Daniele De Martini, James Fulton, Valentina Salvatelli, Chris Bridges, Gonzalo Mateo-Garcia, Valentina Zantedeschi
In this paper, we introduce RaVAEn, a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment.