Zobrazeno 1 - 10
of 1 152
pro vyhledávání: '"Hausler P"'
Publikováno v:
Open Engineering, Vol 14, Iss 1, Pp 127-69 (2024)
Data sheets for 3D printing materials typically include softening temperature, impact strength, tensile strength, and stiffness. However, creep strength, an important parameter for components used over an extended period, is usually not included. Nev
Externí odkaz:
https://doaj.org/article/4f7830e899324d5eb9c03dd0022f4e3a
Autor:
Hausler, Stephen, Moghadam, Peyman
In this work we propose a novel joint training method for Visual Place Recognition (VPR), which simultaneously learns a global descriptor and a pair classifier for re-ranking. The pair classifier can predict whether a given pair of images are from th
Externí odkaz:
http://arxiv.org/abs/2410.06614
Visual Place Recognition (VPR) is a crucial component of many visual localization pipelines for embodied agents. VPR is often formulated as an image retrieval task aimed at jointly learning local features and an aggregation method. The current state-
Externí odkaz:
http://arxiv.org/abs/2409.19293
In this paper, we emphasise the critical importance of large-scale datasets for advancing field robotics capabilities, particularly in natural environments. While numerous datasets exist for urban and suburban settings, those tailored to natural envi
Externí odkaz:
http://arxiv.org/abs/2404.18477
Neural fields provide a continuous scene representation of 3D geometry and appearance in a way which has great promise for robotics applications. One functionality that unlocks unique use-cases for neural fields in robotics is object 6-DoF registrati
Externí odkaz:
http://arxiv.org/abs/2404.18381
Neural fields, coordinate-based neural networks, have recently gained popularity for implicitly representing a scene. In contrast to classical methods that are based on explicit representations such as point clouds, neural fields provide a continuous
Externí odkaz:
http://arxiv.org/abs/2402.09722
Autor:
Vidanapathirana, Kavisha, Knights, Joshua, Hausler, Stephen, Cox, Mark, Ramezani, Milad, Jooste, Jason, Griffiths, Ethan, Mohamed, Shaheer, Sridharan, Sridha, Fookes, Clinton, Moghadam, Peyman
Recent progress in semantic scene understanding has primarily been enabled by the availability of semantically annotated bi-modal (camera and LiDAR) datasets in urban environments. However, such annotated datasets are also needed for natural, unstruc
Externí odkaz:
http://arxiv.org/abs/2312.15364
LiDAR place recognition approaches based on deep learning suffer a significant degradation in performance when there is a shift between the distribution of the training and testing datasets, with re-training often required to achieve top performance.
Externí odkaz:
http://arxiv.org/abs/2308.04638
Autor:
Hausler, Stephen, Garg, Sourav, Chakravarty, Punarjay, Shrivastava, Shubham, Vora, Ankit, Milford, Michael
Can knowing where you are assist in perceiving objects in your surroundings, especially under adverse weather and lighting conditions? In this work we investigate whether a prior map can be leveraged to aid in the detection of dynamic objects in a sc
Externí odkaz:
http://arxiv.org/abs/2306.17536
Autor:
Hausler, Stephen, Garg, Sourav, Chakravarty, Punarjay, Shrivastava, Shubham, Vora, Ankit, Milford, Michael
Most 6-DoF localization and SLAM systems use static landmarks but ignore dynamic objects because they cannot be usefully incorporated into a typical pipeline. Where dynamic objects have been incorporated, typical approaches have attempted relatively
Externí odkaz:
http://arxiv.org/abs/2306.17529