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pro vyhledávání: '"Hausler, Stephen"'
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
Publikováno v:
2023 IEEE International Conference on Robotics and Automation (ICRA)
One recent promising approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques using methods such as SRAL and multi-process fusion. These approaches come with a su
Externí odkaz:
http://arxiv.org/abs/2210.07509
Autor:
Hausler, Stephen, Xu, Ming, Garg, Sourav, Chakravarty, Punarjay, Shrivastava, Shubham, Vora, Ankit, Milford, Michael
6-DoF visual localization systems utilize principled approaches rooted in 3D geometry to perform accurate camera pose estimation of images to a map. Current techniques use hierarchical pipelines and learned 2D feature extractors to improve scalabilit
Externí odkaz:
http://arxiv.org/abs/2206.13883