Visions: A Machine Vision and Automation Solutions Podcast
Aug 11, 2026 · 26 min · 8 segments
Host Jim Tatum interviews Xin Xie, Orbbec Chief Engineer and Director of ODM/OEM & Solutions, about a new robot-free, wearable vision platform that captures synchronized RGB, depth, and motion data…
Start off, how are intrinsic and extrinsic calibrations maintained across large fleets of EGO, UMI, and RISCAM devices, and what mechanisms are in place to
So, yeah, that's an important thing we need to maintain because when we start to do the data connection, so that means we want to do that at scale.
So when you are trying to deploy those data acquisition devices at scale, you got to make sure the calibration is stable.
So that requires a calibration between multiple sensors, like RGB cameras, depth cameras, IMUs, and some other sensors.
And you're going to make sure when they look at one motion or one special target, and all different sensors looking at different locations can finally be merged into one single coordinate.
That's one of the OBEX, I would say, advantage from us because we have done so many intrinsic and extrinsic calibrations for our robot customers.
When they deploy those depth sensors on the different kind of AMR or robots, we've done that in the business more than a decade and have a lot of customers using it.
So from there, we've moving our expertise in intrinsic and extrinsic calibration into this data connection error.
And the second is not only just intrinsic and extrinsic, The second aspect will be you need to also make sure the data is consistent over time.
You don't want those things drifting around like the first minute you wear it on, it's stable and with time going and it becomes unstable, you want to avoid that.
So also like a time domain consistency is also a topic that you need to really take care when you have a large amount of devices capturing data.
And for that part, we have our synchronization and the triggering system precise, managed, and also we've done a lot of job with our robot customers.
We provide a microsecond level, accurate, synchronized signal between all the sensors.
What synchronization architecture are you using and what level of temporal accuracy can one of your customers realistically expect in a deployed situation?
Start off, how are intrinsic and extrinsic calibrations maintained across large fleets of EGO, UMI, and RISCAM devices, and what mechanisms are in place to
So, yeah, that's an important thing we need to maintain because when we start to do the data connection, so that means we want to do that at scale.
So when you are trying to deploy those data acquisition devices at scale, you got to make sure the calibration is stable.
So that requires a calibration between multiple sensors, like RGB cameras, depth cameras, IMUs, and some other sensors.
And you're going to make sure when they look at one motion or one special target, and all different sensors looking at different locations can finally be merged into one single coordinate.
That's one of the OBEX, I would say, advantage from us because we have done so many intrinsic and extrinsic calibrations for our robot customers.
When they deploy those depth sensors on the different kind of AMR or robots, we've done that in the business more than a decade and have a lot of customers using it.
So from there, we've moving our expertise in intrinsic and extrinsic calibration into this data connection error.
And the second is not only just intrinsic and extrinsic, The second aspect will be you need to also make sure the data is consistent over time.
You don't want those things drifting around like the first minute you wear it on, it's stable and with time going and it becomes unstable, you want to avoid that.
So also like a time domain consistency is also a topic that you need to really take care when you have a large amount of devices capturing data.
And for that part, we have our synchronization and the triggering system precise, managed, and also we've done a lot of job with our robot customers.
We provide a microsecond level, accurate, synchronized signal between all the sensors.
What synchronization architecture are you using and what level of temporal accuracy can one of your customers realistically expect in a deployed situation?
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