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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…
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…
What you see is what you get: Experience ranking with deep neural dataset-to-dataset similarity for topological localisation
Matthew Gadd, Benjamin Ramtoula, Daniele De Martini +1
Recalling the most relevant visual memories for localisation or understanding a priori the likely outcome of localisation effort against a particular visual memory is useful for ef…
Visual DNA: Representing and Comparing Images using Distributions of Neuron Activations
Benjamin Ramtoula, Matthew Gadd, Paul Newman +1
Selecting appropriate datasets is critical in modern computer vision. However, no general-purpose tools exist to evaluate the extent to which two datasets differ. For this, we prop…