Summary
Dr Sen Wang is a Senior Lecturer (Associate Professor) in Robotics and Autonomous Systems and the Director of the Sense Robotics Lab at the Department of Electrical and Electronic Engineering and I-X, Imperial's cross-college flagship initiative in AI. Previously, he was an Associate Professor at the Edinburgh Centre for Robotics and a post-doctoral researcher at the University of Oxford.
His research sits at the intersection of robotics, computer vision and machine learning, driving robots and intelligent machines to understand and operate autonomously in unstructured, dynamic environments through probabilistic and learning approaches. His main research areas include robot localisation, autonomous navigation, SLAM, robot vision, robot learning and their application on real-world robot systems to help tackle the challenges we face in our society, from climate change to healthcare.
Through the £18M UKRI ORCA Hub (EP/R026173/1 and EP/W001136/1), he led a research team to develop autonomous underwater sensing and robotic technologies for inspection of offshore energy infrastructure, and successfully carry out the first autonomous wind farm foundation inspection at EDF's Blyth Offshore Wind Farm (EDF Release).
He was awarded the 2023 AI Most Influential Scholar Award Honourable Mention in Robotics. He has served as Associate Editors of IEEE Transactions on Automation Science and Engineering, IEEE Robotics and Automation Letters, ICRA and IROS.
A full list of his publications can be found on Google Scholar.
🛑 Recruitment 🛑
We are looking for self-motivated and hard-working students to join our group on robotics research! If you are interested, please see more information here.</
Selected Publications
Journal Articles
Luo D, Zhuang Y, Wang S, 2022, Hybrid sparse monocular visual odometry with online photometric calibration, International Journal of Robotics Research, ISSN:0278-3649, Pages:027836492211077-027836492211077
Hong Z, Petillot Y, Wallace A, et al., 2022, RadarSLAM: A robust simultaneous localization and mapping system for all weather conditions, International Journal of Robotics Research, Vol:41, ISSN:0278-3649, Pages:519-542
Yang B, Wang S, Markham A, et al., 2020, Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction, International Journal of Computer Vision, Vol:128, ISSN:0920-5691, Pages:53-73
Wang S, Clark R, Wen H, et al., 2018, End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks, International Journal of Robotics Research, Vol:37, ISSN:0278-3649, Pages:513-542
Conference
Zhang K, Hong Z, Xu S, et al., CURL: Continuous, Ultra-compact Representation for LiDAR, Robotics: Science and Systems 2022, Robotics: Science and Systems Foundation
Vargas E, Scona R, Willners JS, et al., 2021, Robust Underwater Visual SLAM Fusing Acoustic Sensing, 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE
Sheeny M, De Pellegrin E, Mukherjee S, et al., 2021, RADIATE: A Radar Dataset for Automotive Perception in Bad Weather, 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE
Bo Y, Jianan W, Clark R, et al., 2019, Learning object bounding boxes for 3D instance segmentation on point clouds, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Neural Information Processing Systems Foundation, Inc.
Hong Z, Petillot Y, Lane D, et al., 2019, TextPlace: Visual Place Recognition and Topological Localization Through Reading Scene Texts, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE