activity
20182020
most citedReal-time Kinematic Ground Truth for the Oxford RobotCar Dataset

51 citations · 52 across the 3 of their papers we have counts for

collaborators

9 papers

cs.CY2020

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…

cs.RO2020

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…

cs.CV2020

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…

cs.RO2020

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…

cs.RO2020

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…

cs.RO202051 cited

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…