报告题目：SLAM问题全局最优解分析及其在无人车上的应用

时 间：2018年10月12日（星期五）下午16:30-18:30

地 点：数学馆金融实验室

 报告内容简介：

This talk proves that the optimization problem of one-step point feature Simultaneous Localization and Mapping (SLAM) is equivalent to a nonlinear optimization problem of a single variable when the associated uncertainties can be described using spherical covariance matrices. Furthermore, it is proven that this optimization problem has at most two minima. The necessary and sufficient conditions for the existence of one or two minima are derived in a form that can be easily evaluated using observation and odometry data. It is demonstrated that more than one minimum exists only when the observation and odometry data are extremely inconsistent with each other. In addition, some techniques including SLAM applied in driverless cars will be reported.  

报告人简介：

Wang Heng received the Bachelor’s degree and the Ph.D. degree from the College of Information Science and Engineering at Northeastern University, PR China in 2003 and 2008, respectively. He is currently a Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China. He was a Post-Doc Research Fellow at the Centre for Autonomous Systems, University of Technology, Sydney from July 2010 to July 2011, and a Lecturer at College of Electronic Information and Control Engineering, Beijing University of Technology, China from July 2008 to March 2014. From March 2014 to November 2015, he worked as a Research Scientist at Institute for Infocomm Research (I2R), A*STAR, Singapore, and from December 2015 to April 2018, he worked as an Associate Professor at College of Information Science and Engineering of Northeastern University, China. His current research interests include fault detection, robust control, and mobile robots simultaneous localization and mapping (SLAM) etc.