1 hr 2 min
SLAM and the Evolution of Spatial AI Unboxing AI: The Podcast for Computer Vision Engineers
-
- Science
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 it's impacted by new technologies like NeRF and what is the current state-of-the-art.
Timestamps and Topics
[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
Interesting Links:
Andrew Blake
Hugh Durrant-Whyte
John Leonard
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
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 - SLAM (Simultaneous Localisation and Mapping) and has continued to develop SLAM 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. SLAM has evolved into a whole new domain of “Spatial AI” leveraging neural implicit representations and the suite of cutting-edge methods creating a full coherent representation of the real world from video.
About the Host
I'm Gil Elbaz, co-founder and CTO of Datagen. 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.
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 it's impacted by new technologies like NeRF and what is the current state-of-the-art.
Timestamps and Topics
[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
Interesting Links:
Andrew Blake
Hugh Durrant-Whyte
John Leonard
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
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 - SLAM (Simultaneous Localisation and Mapping) and has continued to develop SLAM 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. SLAM has evolved into a whole new domain of “Spatial AI” leveraging neural implicit representations and the suite of cutting-edge methods creating a full coherent representation of the real world from video.
About the Host
I'm Gil Elbaz, co-founder and CTO of Datagen. 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.
1 hr 2 min