Mapping, Localization, and Self-Driving Vehicles

Tuesday, March 17, 2015 - 4:00pm

Event Calendar Category

LIDS Seminar Series

Speaker Name

John Leonard



Building and Room Number



This talk will discuss the critical role of mapping and localization in the development of self-driving vehicles. After a discussion of some of the recent amazing progress and open technical challenges in the development of self-driving vehicles, we will discuss the past, present and future of Simultaneous Localization and Mapping (SLAM) in robotics.  We will review the history of SLAM research and will discuss some of the major challenges in SLAM, including choosing a map representation, developing algorithms for efficient state estimation, and solving for data association and loop closure. We will also present recent results on real-time dense mapping using RGB-D cameras and object-based mapping in dynamic environments.

Joint work with Tom Whelan, Michael Kaess, John McDonald, Hordur Johannsson, Maurice Fallon, David Rosen, Mark VanMiddlesworth, Ross Finman, Paul Huang, Liam Paull, Dehann Fourie, and Seth Teller.


John J. Leonard is the Samuel C. Collins Professor of Mechanical and Ocean Engineering and Associate Department Head for Research in the MIT Department of Mechanical Engineering.  He is also a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research addresses the problems of navigation and mapping for autonomous mobile robots. He holds the degrees of B.S.E.E. in Electrical Engineering and Science from the University of Pennsylvania (1987) and D.Phil. in Engineering Science from the University of Oxford (1994). Prof. Leonard joined the MIT faculty in 1996, after five years as a Post-Doctoral Fellow and Research Scientist in the MIT Sea Grant Autonomous Underwater Vehicle (AUV) Laboratory.  He is the recipient of an NSF Career Award (1998) and the King-Sun Fu Memorial Best Transactions on Robotics Paper Award (2006). He is an IEEE Fellow (2014).

Reception information

Reception to follow.