5 citations · 9 across the 7 of their papers we have counts for
15 papers
Fast-MbyM: Leveraging Translational Invariance of the Fourier Transform for Efficient and Accurate Radar Odometry
Robert Weston, Matthew Gadd, Daniele De Martini +2
Masking By Moving (MByM), provides robust and accurate radar odometry measurements through an exhaustive correlative search across discretised pose candidates. However, this dense…
Unsupervised Change Detection of Extreme Events Using ML On-Board
Vít Růžička, Anna Vaughan, Daniele De Martini +5
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…
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…