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Descriptellation
(2022)arXivCurrent descriptors for global localization often struggle under vast viewpoint or appearance changes. One possible improvement is the addition of topological information on semantic objects. However, handcrafted topological descriptors are hard to tune and not robust to environmental noise, drastic perspective changes, object occlusion or misdetections. To solve this problem, we formulate a learning-based approach by modelling semantically ...Working Paper -
Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes
(2019)arXivDeep learning has enabled remarkable advances in scene understanding, particularly in semantic segmentation tasks. Yet, current state of the art approaches are limited to a closed set of classes, and fail when facing novel elements, also known as out of distribution (OoD) data. This is a problem as autonomous agents will inevitably come across a wide range of objects, all of which cannot be included during training. We propose a novel ...Working Paper -
A complete system for vision-based micro-aerial vehicle mapping, planning, and flight in cluttered environments
(2018)arXivWorking Paper -
Redundant Perception and State Estimation for Reliable Autonomous Racing
(2018)arXivWorking Paper -
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An informative path planning framework for UAV-based terrain monitoring
(2018)arXivWorking Paper -
An overview of perception methods for horticultural robots: From pollination to harvest
(2018)arXivWorking Paper -
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