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pro vyhledávání: '"Ramachandran, Saravanabalagi"'
Hierarchical topological representations can significantly reduce search times within mapping and localization algorithms. Although recent research has shown the potential for such approaches, limited consideration has been given to the suitability a
Externí odkaz:
http://arxiv.org/abs/2404.05023
Motion segmentation is a complex yet indispensable task in autonomous driving. The challenges introduced by the ego-motion of the cameras, radial distortion in fisheye lenses, and the need for temporal consistency make the task more complicated, rend
Externí odkaz:
http://arxiv.org/abs/2401.00910
Publikováno v:
The 24th Irish Machine Vision and Image Processing Conference (IMVIP), 2022, 9-16
Scene categorization is a useful precursor task that provides prior knowledge for many advanced computer vision tasks with a broad range of applications in content-based image indexing and retrieval systems. Despite the success of data driven approac
Externí odkaz:
http://arxiv.org/abs/2210.14981
Autor:
Ramachandran, Saravanabalagi, Sistu, Ganesh, Kumar, Varun Ravi, McDonald, John, Yogamani, Senthil
Object detection is a comprehensively studied problem in autonomous driving. However, it has been relatively less explored in the case of fisheye cameras. The strong radial distortion breaks the translation invariance inductive bias of Convolutional
Externí odkaz:
http://arxiv.org/abs/2206.12912
We present the WoodScape fisheye semantic segmentation challenge for autonomous driving which was held as part of the CVPR 2021 Workshop on Omnidirectional Computer Vision (OmniCV). This challenge is one of the first opportunities for the research co
Externí odkaz:
http://arxiv.org/abs/2107.08246
OdoViz is a reactive web-based tool for 3D visualization and processing of autonomous vehicle datasets designed to support common tasks in visual place recognition research. The system includes functionality for loading, inspecting, visualizing, and
Externí odkaz:
http://arxiv.org/abs/2107.07557
Publikováno v:
Session 2: Deep Learning for Computer Vision
Place recognition in a visual SLAM system helps build and maintain a map from multiple traversals of the same environment while closing loops to correct drift accumulated over time. Despite the marked success in visual place recognition research over
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::36d3f928050cdd59e557fdefb889499a
http://mural.maynoothuniversity.ie/10987/
http://mural.maynoothuniversity.ie/10987/
Autor:
Ramachandran, Saravanabalagi
Place recognition in a visual SLAM system helps build and maintain a map from multiple traversals of the same environment while closing loops to correct drift accumulated over time. Despite the marked success in visual place recognition research over
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d43841b2781e88606ec91e3159baeaf8