2 citations · 3 across the 16 of their papers we have counts for
8 papers · 1 filter
GraphSCENE: On-Demand Critical Scenario Generation for Autonomous Vehicles in Simulation
Efimia Panagiotaki, Georgi Pramatarov, Lars Kunze +1
Testing and validating Autonomous Vehicle (AV) performance in safety-critical and diverse scenarios is crucial before real-world deployment. However, manually creating such scenari…
NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms
Efimia Panagiotaki, Daniele De Martini, Lars Kunze +2
This study explores the intersection of neural networks and classical robotics algorithms through the Neural Algorithmic Reasoning (NAR) blueprint, enabling the training of neural…
Watching Grass Grow: Long-term Visual Navigation and Mission Planning for Autonomous Biodiversity Monitoring
Matthew Gadd, Daniele De Martini, Luke Pitt +10
We describe a challenging robotics deployment in a complex ecosystem to monitor a rich plant community. The study site is dominated by dynamic grassland vegetation and is thus visu…
VDNA-PR: Using General Dataset Representations for Robust Sequential Visual Place Recognition
Benjamin Ramtoula, Daniele De Martini, Matthew Gadd +1
This paper adapts a general dataset representation technique to produce robust Visual Place Recognition (VPR) descriptors, crucial to enable real-world mobile robot localisation. T…
That's My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation
Georgi Pramatarov, Matthew Gadd, Paul Newman +1
This paper is about 3D pose estimation on LiDAR scans with extremely minimal storage requirements to enable scalable mapping and localisation. We achieve this by clustering all poi…
OORD: The Oxford Offroad Radar Dataset
Matthew Gadd, Daniele De Martini, Oliver Bartlett +11
There is a growing academic interest as well as commercial exploitation of millimetre-wave scanning radar for autonomous vehicle localisation and scene understanding. Although seve…
Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data
David S. W. Williams, Daniele De Martini, Matthew Gadd +1
Knowing when a trained segmentation model is encountering data that is different to its training data is important. Understanding and mitigating the effects of this play an importa…
Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling
David S. W. Williams, Matthew Gadd, Paul Newman +1
This work proposes a semantic segmentation network that produces high-quality uncertainty estimates in a single forward pass. We exploit general representations from foundation mod…