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thank you for this amazing work!

Pretty nice (:

optimize it to use this for bed leveling in 3D printers other uses are pointless and stupid

Very cool. You could make a MIDI controller as a demo. Something like a theremin. It might also be nice to arrange a few of these in a hemispherical or triangulated configurations for a desktop device. Then you could use both hands as manipulators for a 3D input device. Also use with prosthetics. Really cool stuff guys 👍

Pretty cool stuff here!

آYou could easily do the same with the VL53L5X sensor

Great Work!

Just got a ur10e for R&D, can’t wait to try this

Would just like to point out: Arduino is NOT a microcontroller. It is a framework and development environment with certain compatible/compliant development boards containing a microcontroller from different companies such as Atmel and STM.

I'd be interested in seeing a version of this maximizing for linear accuracy. The setup I envision is the sensor is mounted to a linear rail with a board/reflector fixed at a right angle to one end. Then the sensor is set at a known distance to the board, the differentiation is run with known values for the distance/angle to calibrate for the surface reflectance. Finally the sensor runs on the linear rail, with the calibrated data and the fixed angle relative to the surface to provide only distance data. I'm curious how much all this boiling down of the data will increase the accuracy of the linear distance measurement. Reason being is that I've been interested in the idea of using these types of sensors for closed loop control of a 3d printer (I like the idea of knowing exactly where the print head is at a given time), but one issue is the accuracy. The sensor frequency would definitely be too slow but there are other comments addressing it that could be explored.

Cool!

YES!!! Amazing research guys! Keep it up!

I'm immediately thinking of all the super low cost ways you could now provide backup orientation data to an IMU. With the simplicity and price-point, you could use it to provide a crosscheck to combat sensor drift over long trips. If the sample rate is too low, use two! haha. So many lunar landers have gotten turned around over the years due to sensor confusion, and here's a few dollars that can provide surface geometry data that might be used to reorient. Really neat project!

This would be great to use with my thrust vectoring rocket.

When is this going to be in the next Samsung or Pixel, 6 months? Next Apple 5 years? next LG last year? "Ai assisted 3D modeling" next feature on a smart phone.

Very informative video. Thank you! Enjoyed it a lot.

Wow! This would be useful to implement as part of an automatic bed leveling routine on a 3D printer! Nice work! Subscribed!

What's the model of the robotic arm? It's really cool.

I've been looking for a solution like this - excellent work! Thank you

Is this a similar principle to RADAR and ultrasound scanners?

Surprised these sensors evolved into low resolution 3x3 cameras. Definitely an advantage to apply offboard computing power to the histogram analysis. The integrated processor is a Cortex M0.

To clarify something we've seen many comments on: our results don't mean that the manufacturer's solution for distance estimation is "bad". The manufacturer's solution runs onboard the sensor, which has a minuscule amount of computing power, and was likely designed to support generic use cases for their sensor, not the very specific planar recovery that we perform.

3:55 wow your results are MUCH better than the proprietary distance estimates

Nice - would love to play with that data in Python

Works like echolacation. Animals can do echolocation to detect shape, distance, and texture.

this is awesome, earned a sub.

Excellent work. Impressive signal use.

what about the Sun?

Wow! Kudos guys! 👏

Will adding more sensors, in possibly different angles, improve lower distance error across different materials?

cool!

I wonder if these are the new "induction" previously PIR sensor light sensors to detect a passing human and turn a light on?

aaaand ur a virgin

Brilliant work! I was surprised how well simple calibrated peak detection worked, compared to the much more complicated differentiable method.

Really smart! Thanks for sharing the rationale and code

Very good work guys. Outperforming a company this way makes me wonder if they let the interns write codes for these things.

Very inspiring video :3

Very cool. Looking forward to seeing if you can get geometry reconstruction working!

Fantastic. Also the clarity of your technical descriptions overcame my youtube induced ADHD

Thanks for sharing this.

Very interesting. Looking forward to future videos involving this sensor.

One small step towards no longer having to pay tips to human waiters.

Two questions: - Is there source code available for just the plane reconstruction (excluding robot moving parts or such)? - Is there similar solution for higher range similar sensors? Would be pretty interested if there is and might have use case for AR. Maybe sennsor range is high already but not sure. I know some of these ToF sensors have longer range like around 10m at most (which would be enough for my case) but I guess maybe this exact sensor has smaller range.

I wonder if this could be used to make a device for 3d printer bed leveling to either make a bed mesh or for real time manual bed leveling. This is very cool.

hooooley crap! who knew simple tof sensors had so much output you could gather such useful data from! thank you for this!

the source code is missing

1:55. The blue pane doesn’t match the table. It looks like the bottom left corner should be further away.

Incredible work thanks for sharing!

Nice, This is a cool work around fur sure.

I’ve been longing for an affordable, compact 3D scanner/solid state lidar for years. So many things you could do with something like this. I use a lot of single point ToF sensors in my projects, didn’t know there were 9 points measurement ones. Maybe by having a small array of them, each at a small angle, you could create a working 3D sensor? Video is incomplete. Need to see more