Chris Xiaoxuan Lu

Associate Professor @ Department of Computer Science, University College London

I am an Associate Professor in Robotics and AI at the Department of Computer Science, University College London (UCL). I delve into the dynamic field of spatial AI. My expertise encompasses developing autonomous vehicles and mixed/augmented reality (MR/AR) and IoT devices using AI methods, with a keen focus on elevating their spatial perception ability in 3D environments. My research unfolds across three pivotal dimensions:

  1. Pose Estimation – Determining the orientation and position of moving agents in the wild.
  2. Mapping and Reconstruction – Creating generalisable and expressive representations of environments.
  3. Scene Understanding – Interpreting and making sense of complex and dynamic scenes on-the-fly.

At the core of my research is tackling real-world robustness challenges imposed on spatial perception. This includes overcoming hurdles like visual degradation, GPS denial, navigating the limitations of available resources, and handling unpredictable, out-of-distribution samples. Discover more about my research projects on my research page.

Before coming to UCL, I was a faculty member at the University of Edinburgh and University of Liverpool. I did both my PhD study and post-doctoral in the Department of Computer Science, University of Oxford. Even earlier, I received my M.Eng degree from Nanyang Technological University (NTU).

news

Jan 29, 2024 🎉 Three papers accepted to ICRA-2024. Stay tuned for the code and video release. See you at Yokohama in May.

selected publications

  1. ICRA’24
    Moving Object Detection and Tracking with 4D Radar Point Cloud
    Zhijun Pan, Fangqiang Ding, Hantao Zhong, and Chris Xiaoxuan Lu
    In IEEE International Conference on Robotics and Automation 2024
    arXiv Video Code
  2. IoT-J
    CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals
    Peijun Zhao, Chris Xiaoxuan Lu, Bing Wang, Niki Trigoni, and Andrew Markham
    IEEE Internet of Things Journal 2023
    arXiv Code
  3. CVPR’23
    Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision
    Fangqiang Ding, Andras Palffy, Dariu M. Gavrila, and Chris Xiaoxuan Lu
    In IEEE Conference on Computer Vision and Pattern Recognition 2023
    CVPR’23 Highlight
    arXiv Video Code Website
  4. T-RO
    Graph-Based Thermal–Inertial SLAM With Probabilistic Neural Networks
    Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu, Pedro Porto B. Gusmao, Bing Wang, Andrew Markham, and Niki Trigoni
    IEEE Transactions on Robotics 2022
    arXiv Video Code
  5. ICRA’22
    AutoPlace: Robust Place Recognition with Single-chip Automotive Radar
    Kaiwen Cai, Bing Wang, and Chris Xiaoxuan Lu
    In International Conference on Robotics and Automation (ICRA) 2022
    arXiv Video Code Website
  6. MobiSys’20
    See through smoke: robust indoor mapping with low-cost mmWave radar
    Chris Xiaoxuan Lu, Stefano Rosa, Peijun Zhao, Bing Wang, Changhao Chen, John A. Stankovic, Niki Trigoni, and Andrew Markham
    In The 18th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys) 2020
    arXiv HTML Video Code
  7. SenSys’20
    milliEgo: single-chip mmWave radar aided egomotion estimation via deep sensor fusion
    Chris Xiaoxuan Lu, Muhamad Risqi U. Saputra, Peijun Zhao, Yasin Almalioglu, Pedro P. B. Gusmao, Changhao Chen, Ke Sun, Niki Trigoni, and Andrew Markham
    In The 18th ACM Conference on Embedded Networked Sensor Systems (SenSys) 2020
    arXiv HTML Video Code
  8. MobiCom’18
    Simultaneous Localization and Mapping with Power Network Electromagnetic Field
    Chris Xiaoxuan Lu, Yang Li, Peijun Zhao, Changhao Chen, Linhai Xie, Hongkai Wen, Rui Tan, and Niki Trigoni
    In The 24th Annual International Conference on Mobile Computing and Networking (MobiCom) 2018
    Video PDF
  9. AAAI’18
    IONet: Learning to Cure the Curse of Drift in Inertial Odometry
    Changhao Chen, Chris Xiaoxuan Lu, Andrew Markham, and Niki Trigoni
    In The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI) 2018
    arXiv HTML Video