3D machine vision color cameras for industrial robots.
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Free White Paper

"I can see it, but can't pick it"

Understanding accuracy in vision-guided pick and place

This white paper covers

  • The fundamentals of 3D vision accuracy
  • The importance of the precision, trueness, and accuracy metrics in robotic pick and place
  • The specifications of Zivid cameras as a model of best-in-class 3D camera performance

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Accuracy pick and place robotics ebook

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"Zivid’s high-quality robot vision cameras can reliably deliver the accurate and detailed point clouds that our deep-learning algorithms require for pick and place operations"


Herbert ten Have, CEO of Fizyr

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Discover the secret to reliable picking

“I can see it but can’t pick it": that was something a very important customer told us. You have probably come across these same errors - or “mispicks” - while operating a robot. What is the source of these errors? How can we make the picking performance more reliable?

Trueness, or more specifically, low trueness error, is fundamental to accurate and reliable picking in robot cells. But what is trueness? And why does the explicit split between precision and trueness in accuracy makes so much sense in the field of robotic pick and place?

We have introduced a new way of characterizing and specifying 3D camera performance, and this is what this white paper is all about. We will explain the fundamentals of 3D camera accuracy, while simultaneously discussing the thinking and rationale behind the specifications we use for our 3D cameras. We will cover the importance of precision and trueness in vision-guided applications and illustrate our point by discussing the specifications of Zivid cameras and how they relate to different automation applications.