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Robust Spatial Perception in Degraded Visual Environment

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Feb 01

Date and time: 1 February 2022, 15:00 – 16:00 CET (UTC +1)
Speaker: Chris Xiaoxuan Lu, University of Edinburgh
Title: Robust Spatial Perception in Degraded Visual Environment

Meeting ID: 695 6088 7455
Password: 755440

Administrator: Yulong Gao:

Watch the recorded presentation:


Picture of Chris LuAbstract: From localizing a self-driving vehicle on the street to reconstructing a digital asset through intelligent wearables, spatial perception plays a pivotal role to realize these embodied intelligence systems. While several advances in spatial perception have been witnessed over the last decade, perception robustness against adverse conditions (e.g., sensing degradation) remains a challenge. This talk will present our recent contributions in improving the practical robustness of spatial perception against visual degradation – one of the most common adverse conditions threatening a perception system in the wild. The trajectory of our research is characterized by the usage of increasingly powerful and compact sensors operating in the non-visible spectrum, combining recent breakthroughs in artificial intelligence and multimodal data fusion. Two works focused on 3D localization and environment mapping will be introduced to demonstrate their perception robustness for both mobile robotics and intelligent wearables.

Bio: Xiaoxuan Lu is an assistant professor in the School of Informatics at the University of Edinburgh. He leads a lab researching the autonomy, robustness, and security challenges in emerging cyber-physical systems, such as autonomous driving and mixed reality. Prior to Edinburgh, Dr Lu received his PhD degree from the University of Oxford and an M.Eng degree from Nanyang Technological University. He is a recipient of the Google-DeepMind PhD Scholarship and was selected as one of the Heidelberg Laureate Forum Young Researchers. His research work has constantly appeared in leading venues of applied AI (e.g., CVPR/ICCV/WWW/AAAI) and IoT systems (e.g., MobiCom/MobiSys/SenSys/Ubicomp). This line of work has featured in the popular press incl. The Hacker News, TechXplore, American Security Today, Planet Biometrics and Sputnik etc.

Link to the profile of Chris Xiaoxuan Lu
Twitter: @chris_x_lu
LinkedIn profile