Peize Li

Peize Li

PhD Student

University of Edinburgh

Biography

Hello! This is Peize Li (李沛泽). I am a first-year PhD student in the School of Informatics at the University of Edinburgh. I am a member of Mobile Autonomy, Perception and Sensing (MAPS) Lab led by Dr. Chris Xiaoxuan Lu. My PhD is jointly funded by the School of Informatics and A*STAR Singapore. My research interest lies in robotic perception, with focus on multi-modal odometry, localization and reconstruction.

Previously I obtained my MSc Advanced Design Informatics from the University of Edinburgh while working as Research Intern of Dr. Chris Xiaoxuan Lu. I obtained my BSc degree in Physics at Yuanpei College of Peking Univeristy with research experience in quatum materials and high-energy physics. Between degrees, I worked as a Data Scientist at startup company Huarui.AI in Beijing and a Research Intern at Huawei UK R&D in Edinburgh.

Interests
  • Robotic Perception
  • Edge Computing
  • Simultaneous Localization and Mapping
  • 3D Reconstruction
Education
  • PhD Robotics, 2026 (expected)

    University of Edinburgh

  • MSc Advanced Design Informatics, 2022

    University of Edinburgh

  • BSc Physics, 2019

    Peking University

Recent News

[01/10/2022] I started my PhD with the University of Edinburgh at IPAB: Robotics, Computer Vision, Computer Graphics and Animation

[30/07/2022] New paper on OdomBeyondVision: An Indoor Multi-modal Multi-platform Odometry Dataset Beyond the Visible Spectrum accepted by IROS 2022.

[11/07/2022] I graduated with Distinction and Dissertation Prize for best performing dissertation from MSc Advanced Design Informatics.

[17/10/2021] New paper on Motion Tracklet Oriented 6-DoF Inertial Tracking Using Commodity Smartphones accepted by SenSys DATA 2021.

[17/07/2021] Our team won 1st place in Arm High-Performance-Computing (HPC) Hackathon held by Arm and AWS.

Recent Publications

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(2022). OdomBeyondVision: An Indoor Multi-modal Multi-platform Odometry Dataset Beyond the Visible Spectrum. 2022 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS).

DOI

(2021). Motion Tracklet Oriented 6-DoF Inertial Tracking Using Commodity Smartphones. Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems.

DOI