SLAM and the Evolution of Spatial AI with Andrew J. Davison

ABSTRACT

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

[00:00:00] Intro
[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 Twitter  Website

SLAM

MonoSLAM 

Video: MonoSLAM

Steven J. Lovegrove

Alex  Mordvintsev

Prof. David Murray

Richard Newcombe

Renato Salas-Moreno 

Andrew Zisserman

A visual introduction to Gaussian Belief Propagation

Github Gaussian Belief Propagation

A Robot Web for Distributed Many-Device Localisation

In-Place Scene Labelling and Understanding with Implicit Scene Representation

Video: In-Place Scene Labelling and Understanding with Implicit Scene Representation

Video: Robotic manipulation of object using SOTA

Andrew Reacting to NERF in 2020

Cellular automata

Neural cellular automata

Dyson Robotics

GUEST BIO

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.

I’m Gil Elbaz, Co-founder and CTO of Datagen. In this podcast, I speak with interesting computer vision thinkers and practitioners. I ask the big questions that touch on the issues and challenges that ML and CV engineers deal with every day. On the way, I hope you uncover a new subject or gain a different perspective, as well as enjoying engaging conversation. It’s about much more than the technical processes – it’s about people, journeys, and ideas. Turn up the volume, insights inside.
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