Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

  • 4 Similar Profiles
Sensors Engineering & Materials Science
Autonomous underwater vehicles Engineering & Materials Science
Robots Engineering & Materials Science
Observability Engineering & Materials Science
Neural networks Engineering & Materials Science
Extended Kalman filters Engineering & Materials Science
Robotics Engineering & Materials Science
learning Physics & Astronomy

Co Author Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2010 2018

Defo-Net: Learning Body Deformation using Generative Adversarial Networks

Wang, Z., Rosa, S., Xie, L., Yang, B., Wang, S., Trigoni, N. & Markham, A. 15 Jan 2018 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE

Research output: Chapter in Book/Report/Conference proceedingConference contribution

UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning

Li, R., Wang, S., Long, Z. & Gu, D. 15 Jan 2018 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3D Object Reconstruction from a Single Depth View with Adversarial Learning

Yang, B., Wen, H., Wang, S., Clark, R., Markham, A. & Trigoni, N. 19 Aug 2017 2017 IEEE International Conference on Computer Vision (ICCV) Workshop. IEEE

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Open Access
File

DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks

Wang, S., Clark, R., Wen, H. & Trigoni, N. 24 Jul 2017 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, p. 2043-2050 8 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Neural networks
Pipelines
learning
experiment
Feature matching

End-to-End, Sequence-to-Sequence Probabilistic Visual Odometry through Deep Neural Networks

Wang, S., Clark, R., Wen, H. & Trigoni, N. 16 Oct 2017 In : International Journal of Robotics Research.

Research output: Contribution to journalArticle

Open Access
File
Neural networks
Recurrent neural networks
Computer vision
Robotics
Pipelines