Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Muhamad Risqi U Saputra"'
Autor:
Zhuangzhuang Dai, Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu, Vu Tran, L. N. S. Wijayasingha, M. Arif Rahman, John A. Stankovic, Andrew Markham, Niki Trigoni
Publikováno v:
2022 Workshop on Cyber Physical Systems for Emergency Response (CPS-ER).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::32c3008351d17bc6dd31a0b8c6139dd1
https://doi.org/10.1109/cps-er56134.2022.00007
https://doi.org/10.1109/cps-er56134.2022.00007
Autor:
Andrew Markham, Changhao Chen, Stefano Rosa, Niki Trigoni, Wei Wang, Pedro P. B. de Gusmao, Yasin Almalioglu, Chris Xiaoxuan Lu, Muhamad Risqi U. Saputra, Johan Wahlstrom
Publikováno v:
IEEE Robotics and Automation Letters
Visual odometry shows excellent performance in a wide range of environments. However, in visually-denied scenarios (e.g. heavy smoke or darkness), pose estimates degrade or even fail. Thermal cameras are commonly used for perception and inspection wh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::31a7cb83703e5093d0502210370e01f6
https://doi.org/10.1109/lra.2020.2969170
https://doi.org/10.1109/lra.2020.2969170
Autor:
Yasin Almalioglu, Mehmet Turan, Muhamad Risqi U. Saputra, Pedro P.B. de Gusmão, Andrew Markham, Niki Trigoni
Publikováno v:
Neural networks : the official journal of the International Neural Network Society. 150
In the last decade, numerous supervised deep learning approaches have been proposed for visual-inertial odometry (VIO) and depth map estimation, which require large amounts of labelled data. To overcome the data limitation, self-supervised learning h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4becd349c4dd76b87beb15cadbe61d16
https://pubmed.ncbi.nlm.nih.gov/35313245
https://pubmed.ncbi.nlm.nih.gov/35313245
Publikováno v:
SECON
In cloud-edge environments, running all Deep Neural Network (DNN) models on the cloud causes significant network congestion and high latency, whereas the exclusive use of the edge device for execution limits the size and structure of the DNN, impacti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::adb5eb758c3a6c0589a736a42ad384af
https://doi.org/10.1109/secon52354.2021.9491600
https://doi.org/10.1109/secon52354.2021.9491600
Autor:
Pedro P. B. de Gusmao, Chris Xiaoxuan Lu, Niki Trigoni, Bing Wang, Andrew Markham, Muhamad Risqi U. Saputra
Publikováno v:
Saputra, M R U, Lu, C X, de Gusmao, P P B, Wang, B, Markham, A & Trigoni, N 2022, ' Graph-Based Thermal–Inertial SLAM With Probabilistic Neural Networks ', IEEE Transactions on Robotics, vol. 38, no. 3, pp. 1875-1893 . https://doi.org/10.1109/TRO.2021.3120036
Simultaneous Localization and Mapping (SLAM) system typically employ vision-based sensors to observe the surrounding environment. However, the performance of such systems highly depends on the ambient illumination conditions. In scenarios with advers
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::411e62e5164116b2987fba155074c6da
http://arxiv.org/abs/2104.07196
http://arxiv.org/abs/2104.07196
Publikováno v:
SenSys
Positional estimation is of great importance in the public safety sector. Emergency responders such as fire fighters, medical rescue teams, and the police will all benefit from a resilient positioning system to deliver safe and effective emergency se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c706c96755d92864361d9df7425d3f8b
https://doi.org/10.1145/3384419.3430421
https://doi.org/10.1145/3384419.3430421
Publikováno v:
ACM Computing Surveys. 51:1-36
In the last few decades, Structure from Motion (SfM) and visual Simultaneous Localization and Mapping (visual SLAM) techniques have gained significant interest from both the computer vision and robotic communities. Many variants of these techniques h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d46fce937bd77108116e0d71515bebee
https://doi.org/10.1145/3177853
https://doi.org/10.1145/3177853
Autor:
Pedro P. B. de Gusmao, Peijun Zhao, Muhamad Risqi U. Saputra, Andrew Markham, Changhao Chen, Yasin Almalioglu, Ke Sun, Chris Xiaoxuan Lu, Niki Trigoni
Publikováno v:
Lu, C X, Saputra, M R U, Zhao, P, Almalioglu, Y, de Gusmao, P P B, Chen, C, Sun, K, Trigoni, N & Markham, A 2020, MilliEgo: Single-Chip MmWave Radar Aided Egomotion Estimation via Deep Sensor Fusion . in Proceedings of the 18th Conference on Embedded Networked Sensor Systems . New York, NY, USA, pp. 109–122, 18th ACM Conference on Embedded Networked Sensor Systems, Yokohama, Japan, 16/11/20 . https://doi.org/10.1145/3384419.3430776
SenSys
SenSys
Robust and accurate trajectory estimation of mobile agents such as people and robots is a key requirement for providing spatial awareness for emerging capabilities such as augmented reality or autonomous interaction. Although currently dominated by o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::421dfad29ec85eaecba02d2e86779761
Autor:
Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Yasin Almalioglu, Andrew Markham, Niki Trigoni
Publikováno v:
ICCV
This paper presents a novel method to distill knowledge from a deep pose regressor network for efficient Visual Odometry (VO). Standard distillation relies on "dark knowledge" for successful knowledge transfer. As this knowledge is not available in p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c1be124c11da9ce0eddb9b68f0e700a
https://doi.org/10.1109/iccv.2019.00035
https://doi.org/10.1109/iccv.2019.00035
Publikováno v:
ICRA
Inspired by the cognitive process of humans and animals, Curriculum Learning (CL) trains a model by gradually increasing the difficulty of the training data. In this paper, we study whether CL can be applied to complex geometry problems like estimati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99460744008b6854788065d88d6c7f92
http://arxiv.org/abs/1903.10543
http://arxiv.org/abs/1903.10543