• SLAM and the Evolution of Spatial AI

  • 2022/11/07
  • 再生時間: 1 時間 3 分
  • ポッドキャスト

SLAM and the Evolution of Spatial AI

  • サマリー

  • 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.


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あらすじ・解説

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.


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