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of 15
pro vyhledávání: '"Knights, Joshua"'
This paper proposes SOLVR, a unified pipeline for learning based LiDAR-Visual re-localisation which performs place recognition and 6-DoF registration across sensor modalities. We propose a strategy to align the input sensor modalities by leveraging s
Externí odkaz:
http://arxiv.org/abs/2409.10247
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
This paper proposes a pose-graph attentional graph neural network, called P-GAT, which compares (key)nodes between sequential and non-sequential sub-graphs for place recognition tasks as opposed to a common frame-to-frame retrieval problem formulatio
Externí odkaz:
http://arxiv.org/abs/2309.00168
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:
Knights, Joshua, Vidanapathirana, Kavisha, Ramezani, Milad, Sridharan, Sridha, Fookes, Clinton, Moghadam, Peyman
Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning based approaches. Natural and unstructured environments present many addit
Externí odkaz:
http://arxiv.org/abs/2211.12732
State-of-the-art lidar place recognition models exhibit unreliable performance when tested on environments different from their training dataset, which limits their use in complex and evolving environments. To address this issue, we investigate the t
Externí odkaz:
http://arxiv.org/abs/2210.01361
Place recognition is a fundamental component of robotics, and has seen tremendous improvements through the use of deep learning models in recent years. Networks can experience significant drops in performance when deployed in unseen or highly dynamic
Externí odkaz:
http://arxiv.org/abs/2203.00807
Point clouds are a key modality used for perception in autonomous vehicles, providing the means for a robust geometric understanding of the surrounding environment. However despite the sensor outputs from autonomous vehicles being naturally temporal
Externí odkaz:
http://arxiv.org/abs/2112.00289
Autor:
Knights, Joshua, Harwood, Ben, Ward, Daniel, Vanderkop, Anthony, Mackenzie-Ross, Olivia, Moghadam, Peyman
This paper presents TCE: Temporally Coherent Embeddings for self-supervised video representation learning. The proposed method exploits inherent structure of unlabeled video data to explicitly enforce temporal coherency in the embedding space, rather
Externí odkaz:
http://arxiv.org/abs/2004.02753
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