51 citations · 54 across the 11 of their papers we have counts for
34 papers
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…
Self-Supervised Localisation between Range Sensors and Overhead Imagery
Tim Y. Tang, Daniele De Martini, Shangzhe Wu +1
Publicly available satellite imagery can be an ubiquitous, cheap, and powerful tool for vehicle localisation when a prior sensor map is unavailable. However, satellite images are n…
Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios
Matthew Gadd, Daniele De Martini, Letizia Marchegiani +2
This paper discusses ongoing work in demonstrating research in mobile autonomy in challenging driving scenarios. In our approach, we address fundamental technical issues to overcom…