Zobrazeno 1 - 10
of 2 811
pro vyhledávání: '"de Martini A"'
In recent years, the serverless paradigm has been widely adopted to develop cloud applications, as it enables building scalable solutions while delegating operational concerns such as infrastructure management and resource provisioning to the serverl
Externí odkaz:
http://arxiv.org/abs/2410.21793
This paper proposes a method for on-demand scenario generation in simulation, grounded on real-world data. Evaluating the behaviour of Autonomous Vehicles (AVs) in both safety-critical and regular scenarios is essential for assessing their robustness
Externí odkaz:
http://arxiv.org/abs/2410.13514
This study explores the intersection of neural networks and classical robotics algorithms through the Neural Algorithmic Reasoning (NAR) framework, allowing to train neural networks to effectively reason like classical robotics algorithms by learning
Externí odkaz:
http://arxiv.org/abs/2410.11031
This paper investigates the utility of Neural Radiance Fields (NeRF) models in extending the regions of operation of a mobile robot, controlled by Image-Based Visual Servoing (IBVS) via static CCTV cameras. Using NeRF as a 3D-representation prior, th
Externí odkaz:
http://arxiv.org/abs/2408.01251
Autor:
Gadd, Matthew, De Martini, Daniele, Pitt, Luke, Tubby, Wayne, Towlson, Matthew, Prahacs, Chris, Bartlett, Oliver, Jackson, John, Qi, Man, Newman, Paul, Hector, Andrew, Salguero-Gómez, Roberto, Hawes, Nick
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 visually ambiguous and liable to drastic appearance change over the course
Externí odkaz:
http://arxiv.org/abs/2404.10446
This paper adapts a general dataset representation technique to produce robust Visual Place Recognition (VPR) descriptors, crucial to enable real-world mobile robot localisation. Two parallel lines of work on VPR have shown, on one side, that general
Externí odkaz:
http://arxiv.org/abs/2403.09025
Autor:
Panagiotaki, Efimia, Reinmund, Tyler, Mouton, Stephan, Pitt, Luke, Shanthini, Arundathi Shaji, Tubby, Wayne, Towlson, Matthew, Sze, Samuel, Liu, Brian, Prahacs, Chris, De Martini, Daniele, Kunze, Lars
This paper introduces RobotCycle, a novel ongoing project that leverages Autonomous Vehicle (AV) research to investigate how road infrastructure influences cyclist behaviour and safety during real-world journeys. The project's requirements were defin
Externí odkaz:
http://arxiv.org/abs/2403.07789
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 points of segmented scans into semantic objects and representing them onl
Externí odkaz:
http://arxiv.org/abs/2403.04755
Autor:
Gadd, Matthew, De Martini, Daniele, Bartlett, Oliver, Murcutt, Paul, Towlson, Matt, Widojo, Matthew, Muşat, Valentina, Robinson, Luke, Panagiotaki, Efimia, Pramatarov, Georgi, Kühn, Marc Alexander, Marchegiani, Letizia, Newman, Paul, Kunze, Lars
There is a growing academic interest as well as commercial exploitation of millimetre-wave scanning radar for autonomous vehicle localisation and scene understanding. Although several datasets to support this research area have been released, they ar
Externí odkaz:
http://arxiv.org/abs/2403.02845
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 important part in their application from a performance and assurance perspect
Externí odkaz:
http://arxiv.org/abs/2402.17653