51 citations · 92 across the 29 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…