讲座题目： A Few Attempts to Improve Robustness of Visual SLAM

讲座时间：2024年6月30日（周日）下午13:30-15:00

讲座地点：北教25-309

主讲人：宋德臻 教授

报告摘要：

When a camera is employed as the primary sensor to perform simultaneous localization and mapping (SLAM) task for a robot or a mobile device, it is often referred to as the visual SLAM approach. Visual SLAM has seen many applications including augmented reality, autonomous driving, and service robotics due to its low cost in sensory hardware and small footprint. It is vital part of navigation and scene reconstruction. However, visual SLAM still suffers from robustness issue due to its reliance on the continuously successful image matching process. Due to lighting, camera perspective, and feature distribution, vSLAM algorithms still have non negligible failure rate. Here we present a few attempts that our lab has tried to attack the robustness issue from multiple angles: exploiting complex feature, spatial knowledge sharing, better robust estimation, and improvement of sparse optimization solver. We present those approaches and hope to encourage discussion and attention to the robustness issue which is the main hurtle in many real-world applications.  

报告人简介：

Dezhen Song is a Professor and Deputy Department Chair with Department of Robotics in MBZ University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE, and a Professor (on leave) and former Associate Department Head for Academics with Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA. Song received his Ph.D. in 2004 from University of California, Berkeley; MS and BS from Zhejiang University in 1998 and 1995, respectively. Song's primary research area is robot perception, networked robots, visual navigation, automation, and stochastic modeling. From 2008 to 2012, Song was an Associate Editor of IEEE Transactions on Robotics (T-RO). From 2010 to 2014, Song was an Associate Editor of IEEE Transactions on Automation Science and Engineering (T-ASE). Song was a Senior Editor for IEEE Robotics and Automation Letters (RA-L) from 2017 to 2021 and currently is a Senior Editor for IEEE Transactions on Automation Science and Engineering (T-ASE). He is also a multimedia editor and chapter author for Springer Handbook of Robotics. His research has resulted in one monograph and more than 138 refereed conferences and journal publications. Dr. Song received the NSF Faculty Early Career Development (CAREER) Award in 2007, the Kayamori Best Paper Award of the 2005 IEEE International Conference on Robotics and Automation (ICRA), the 2022 Best Paper Awards of the LCT 2022 and 2024 Affiliated Conference, the 1st place in the GM/SAE AutodriveChallenge II competition in 2023, and the Amazon Research Award in 2020.

计算机科学与技术学院

2024年6月28日