60 citations · 234 across the 23 of their papers we have counts for
11 papers · 1 filter
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…
Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision
David Williams, Daniele De Martini, Matthew Gadd +2
Reliable outdoor deployment of mobile robots requires the robust identification of permissible driving routes in a given environment. The performance of LiDAR and vision-based perc…
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…
LiDAR Lateral Localisation Despite Challenging Occlusion from Traffic
Tarlan Suleymanov, Matthew Gadd, Lars Kunze +1
This paper presents a system for improving the robustness of LiDAR lateral localisation systems. This is made possible by including detections of road boundaries which are invisibl…
Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance
Matthew Gadd, Daniele De Martini, Paul Newman
This paper details an application which yields significant improvements to the adeptness of place recognition with Frequency-Modulated Continuous-Wave radar - a commercially promis…
Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset
Will Maddern, Geoffrey Pascoe, Matthew Gadd +3
We describe the release of reference data towards a challenging long-term localisation and mapping benchmark based on the large-scale Oxford RobotCar Dataset. The release includes…
RSL-Net: Localising in Satellite Images From a Radar on the Ground
Tim Y. Tang, Daniele De Martini, Dan Barnes +1
This paper is about localising a vehicle in an overhead image using FMCW radar mounted on a ground vehicle. FMCW radar offers extraordinary promise and efficacy for vehicle localis…
Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning
Ştefan Săftescu, Matthew Gadd, Daniele De Martini +2
This paper presents a system for robust, large-scale topological localisation using Frequency-Modulated Continuous-Wave (FMCW) scanning radar. We learn a metric space for embedding…
Learning to Correct 3D Reconstructions from Multiple Views
Ştefan Săftescu, Paul Newman
This paper is about reducing the cost of building good large-scale 3D reconstructions post-hoc. We render 2D views of an existing reconstruction and train a convolutional neural ne…