Changhao Chen
Changhao Chen
Department of Computer Science,
Robert Hooke Building
Directions Postal Address
Biography
I’m a Ph.D. student postdoctoral researcher at the Department of Computer Science, University of Oxford. My research interests include robotics, machine learning, and cyber physical systems. My Ph.D. work was supervised by Prof. Niki Trigoni and Prof. Andrew Markham, and focused on learning based robust localization for intelligent systems.
Driven by solving real-world problems, my research is to develop machine (deep) learning techniques to process time-series sensor data for localization, navigation, mapping and perception, in support of robots, mobile devices, self-driving vehicles and Internet of Things (IoT).
I also have interests on deep probabilistic models, domain adaptaion, state space models, and feature selection.
I worked on state estimation from a variety of sensor modalities, e.g. inertial, visual, magnetic, lidar point-clouds and MMWave radar data.
More details please refer to my personal webpage.
Selected Publications
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DeepTIO: A Deep Thermal−Inertial Odometry with Visual Hallucination
M.R.U. Saputra P.P.B. de Gusmao C.X. Lu Y. Almalioglu S. Rosa C. Chen J. Wahlstrom W. Wang A. Markham and N. Trigoni
In IEEE Robotics and Automation Letters (RAL) + IEEE ICRA. 2020.
Details about DeepTIO: A Deep Thermal−Inertial Odometry with Visual Hallucination | BibTeX data for DeepTIO: A Deep Thermal−Inertial Odometry with Visual Hallucination | Download (pdf) of DeepTIO: A Deep Thermal−Inertial Odometry with Visual Hallucination
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Deep Learning based Pedestrian Inertial Navigation: Methods‚ Dataset and On−Device Inference
Changhao Chen‚ Peijun Zhao‚ Chris Xiaoxuan Lu‚ Wei Wang‚ Andrew Markham and Niki Trigoni
In IEEE Internet of Things Journal. 2020.
Details about Deep Learning based Pedestrian Inertial Navigation: Methods‚ Dataset and On−Device Inference | BibTeX data for Deep Learning based Pedestrian Inertial Navigation: Methods‚ Dataset and On−Device Inference | Download (pdf) of Deep Learning based Pedestrian Inertial Navigation: Methods‚ Dataset and On−Device Inference
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Deep Neural Network Based Inertial Odometry Using Low−cost Inertial Measurement Units
Changhao Chen‚ Chris Xiaoxuan Lu‚ Johan Wahlstrom‚ Andrew Markham and Niki Trigoni
In IEEE Transactions on Mobile Computing. 2020.
Details about Deep Neural Network Based Inertial Odometry Using Low−cost Inertial Measurement Units | BibTeX data for Deep Neural Network Based Inertial Odometry Using Low−cost Inertial Measurement Units | Download (pdf) of Deep Neural Network Based Inertial Odometry Using Low−cost Inertial Measurement Units