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Host Gil Elbaz welcomes Andrew J. Davison, the father of SLAM. Andrew and Gil dive right into how SLAM has evolved and how it started. They speak about Spatial AI and what it means along with a discussion about global belief propagation. Of course, they talk about robotics, how they’re impacted by new technologies like NeRF and what is the current state-of-the-art.
TOPICS & TIMESTAMPS
[00:02:07] Early Research Leading to SLAM
[00:04:49] Why SLAM
[00:08:20] Computer Vision Based SLAM
[00:09:18] MonoSLAM Breakthrough
[00:13:47] Applications of SLAM
[00:16:27] Modern Versions of SLAM
[00:21:50] Spatial AI
[00:26:04] Implicit vs. Explicit Scene Representations
[00:34:32] Impact on Robotics
[00:38:46] Reinforcement Learning (RL)
[00:43:10] Belief Propagation Algorithms for Parallel Compute
[00:50:51] Connection to Cellular Automata
[00:55:55] Recommendations for the Next Generation of Researchers
LINKS AND RESOURCES
Andrew Davison is a professor of Robot Vision at the Department of Computing, Imperial College London. In addition, he is the director and founder of the Dyson robotics laboratory. Andrew pioneered the cornerstone algorithm for robotic vision, gaming, drones and many other applications – SLAM (Simultaneous Localisation and Mapping) and has continued to develop the SLAM direction in substantial ways since then. His research focus is in improving & enhancing SLAM in terms of dynamics, scale, detail level, efficiency and semantic understanding of real-time video. Under Andrew’s guidance SLAM has evolved into a whole new domain of “Spatial AI” leveraging neural implicit representations and the suite of cutting-edge methods to create a full coherent representation of the real world from video.