51 citations · 54 across the 11 of their papers we have counts for
16 papers · 1 filter
Contrastive Learning for Unsupervised Radar Place Recognition
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. Our method is base…
The Oxford Road Boundaries Dataset
Tarlan Suleymanov, Matthew Gadd, Daniele De Martini +1
In this paper we present the Oxford Road Boundaries Dataset, designed for training and testing machine-learning-based road-boundary detection and inference approaches. We have hand…
Unsupervised Place Recognition with Deep Embedding Learning over Radar Videos
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. We experiment on 280…
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning
David Williams, Matthew Gadd, Daniele De Martini +1
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…
RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar
Prannay Kaul, Daniele De Martini, Matthew Gadd +1
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. We a…
Rainy screens: Collecting rainy datasets, indoors
Horia Porav, Valentina-Nicoleta Musat, Tom Bruls +1
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 condit…