This talk will discuss the critical role of mapping and localization in the development of self-driving cars and autonomous underwater vehicles (AUVs). 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 describe some of the challenges using SLAM for AUVs, and we will also present recent results on object-based mapping in dynamic environments and real-time
dense mapping using RGB-D cameras.
Joint work with Sudeep Pillai, Tom Whelan, Michael Kaess, John McDonald, Hordur Johannsson, Maurice Fallon, David Rosen, Ross Finman, Paul Huang, Liam Paull, Nick Wang, and Dehann Fourie.