University of Macau - Hunan University – The Hong Kong Polytechnic University Academic Salon on Autonomous Systems
The first event of University of Macau - Hunan University – The Hong Kong Polytechnic University Academic Salon on Autonomous Systems
The University of Macau - Hunan University – The Hong Kong Polytechnic University Academic Salon on Autonomous Systems successfully launched its first session on December 27. This monthly event, collaboratively organized by three renowned research groups—The Intelligent Machine Research Lab (IMRL) led by Dr. Kong Hui at the University of Macau, The Neuromorphic Automation and Intelligence Lab (NAIL) led by Prof. Zhou Yi at Hunan University, and the Trustworthy AI and Autonomous Systems Lab (TAS LAB) led by Dr. Wen Weisong at The Hong Kong Polytechnic University—aims to foster communication, collaboration, and knowledge exchange among young scholars in the field of autonomous systems.
The first salon featured three presentations, offering diverse perspectives on cutting-edge topics:
Towards LiDAR-Inertial State Estimation and Mapping with Next-Generation LiDAR Systems: Preliminary Explorations and Results
Presented by Mingle Zhao from IMRL, this talk highlighted the potential of Doppler LiDAR in addressing inherent challenges in conventional LiDAR-based state estimation methods. By capturing Doppler velocity in addition to range measurements, Doppler LiDAR opens a novel avenue for robotic sensing, with significant implications for improving accuracy and reliability in motion estimation and mapping.
Motion and Structure from Event-based Normal Flow
Presented by Zhongyang Ren from NAIL, this presentation introduced an innovative approach to recovering camera motion and scene geometry using event-based normal flow. By incorporating a geometric error term, the proposed method achieves efficient and accurate solutions for first-order kinematics and scene geometry, addressing the nonlinear challenges of event camera data processing.
3D LiDAR-aided GNSS Positioning: From Sensor Integration to Signal Restoration
Presented by Xikun Liu from TAS LAB, this talk explored the integration of 3D LiDAR and GNSS for positioning in challenging environments like urban canyons. By leveraging LiDAR to assist GNSS signal processing and restore blocked or reflected signals, the presentation showcased recent advancements and ongoing research in enhancing GNSS positioning accuracy and reliability.
The event featured engaging discussions following each presentation, promoting collaboration and idea exchange among participants. Stay tuned for future sessions as we continue to bring together leading scholars and students in the field of autonomous systems!
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