Benedikt Seidel's banner
Benedikt Seidel's profile picture

Benedikt Seidel

@ben_sdl • 3,624 subscribers

Building models for the physical world. Robotics, ML and Startups

Shorts

I built something that finally gives agents the option to see and measure the physical world, reason within it, and accelerate your business with better and faster decisions. Pulling out insights from 3D data and cross-referencing across data silos, completely automating all the steps. From point cloud to the sent pick-up price, date, and picking the best buyer. If you have other companies our agent should integrate with, tell me!

I built something that finally gives agents the option to see and measure the physical world, reason within it, and accelerate your business with better and faster decisions. Pulling out insights from 3D data and cross-referencing across data silos, completely automating all the steps. From point cloud to the sent pick-up price, date, and picking the best buyer. If you have other companies our agent should integrate with, tell me!

14,021 Aufrufe

Videos

LIVE
1.2k

Anya Rossi

sweetdream.ai

SweetDream.aiSponsored•Livecam
Streaming Now

Watch Anya Live

Anya is streaming live right now! Join her private show and enjoy exclusive content.

HD live stream
Exclusive private shows
1.2k viewers online

Free preview available • Premium content

Day 3 of seeing how we can deploy physical AI in Europe. I was at 11 companies in 2 days, getting to speak to the right person is an art, more here:
3:01
ben_sdl's profile picture

Day 3 of seeing how we can deploy physical AI in Europe. I was at 11 companies in 2 days, getting to speak to the right person is an art, more here:

Benedikt Seidel

145,554 Aufrufe • vor 1 Monat

I am happy to be finally able to post what I was able to build over the last few weeks. A full real-time high-frequency state estimation and mapping algorithm completely written line by line from scratch in Rust, which can be used by robots to navigate and reason within the 3D world also in complicated scenarios. TBH this took me longer than expected (which was still super fast :D) but you need to get a lot right: From the sensors over the drivers to their respective estimation pipeline and then fusing everything together - a covariance nightmare - and something that can be refined over years to come (currently using Fisher Information from the real measurements). What you see here is not the output of some structure from motion or Gaussian splatting, these are the points of a tight mesh (high res for the video) that a robot can use in real time to plan a path using any open-source planner. The flight you experience through the world is the actual state estimate of the scanner which is published at IMU rate. Yes, currently we have some artefacts of filtered-out humans (GDPR compliant of course :) ) and moving cars and there is still some calibration that could be improved. Offline refinement with SFM and Gaussian splats is possible as well but currently not on the road map. What is on the road map is an exciting step of now being able to collect data from customers at construction sites and in warehouses (currently handheld in the near future with a robot). This data can then be used by our physical agents to reason within this world and automate any customer’s task related to 3D data. If you have anyone who wastes time manually looking 👀 through 3D data, or cannot collect enough 3D data and interpret: Tell me how to reach them!
1:15
ben_sdl's profile picture

I am happy to be finally able to post what I was able to build over the last few weeks. A full real-time high-frequency state estimation and mapping algorithm completely written line by line from scratch in Rust, which can be used by robots to navigate and reason within the 3D world also in complicated scenarios. TBH this took me longer than expected (which was still super fast :D) but you need to get a lot right: From the sensors over the drivers to their respective estimation pipeline and then fusing everything together - a covariance nightmare - and something that can be refined over years to come (currently using Fisher Information from the real measurements). What you see here is not the output of some structure from motion or Gaussian splatting, these are the points of a tight mesh (high res for the video) that a robot can use in real time to plan a path using any open-source planner. The flight you experience through the world is the actual state estimate of the scanner which is published at IMU rate. Yes, currently we have some artefacts of filtered-out humans (GDPR compliant of course :) ) and moving cars and there is still some calibration that could be improved. Offline refinement with SFM and Gaussian splats is possible as well but currently not on the road map. What is on the road map is an exciting step of now being able to collect data from customers at construction sites and in warehouses (currently handheld in the near future with a robot). This data can then be used by our physical agents to reason within this world and automate any customer’s task related to 3D data. If you have anyone who wastes time manually looking 👀 through 3D data, or cannot collect enough 3D data and interpret: Tell me how to reach them!

Benedikt Seidel

16,671 Aufrufe • vor 3 Monaten

Keine weiteren Inhalte verfügbar