See how color 3D vision helps you to succeed with pick and place robotics

Better random bin picking with 3D machine vision color cameras

Most articles and literature you'll find describes random 3D bin-picking using machine vision as the ultimate challenge for industrial and pick and place robot automation. Detecting and recognizing objects, then correctly picking and placing them might seem trivial for us humans with a brain, eyes, and hands.

However, for most automation tasks, strict control of the operation is required for successful and repetitive operation. With random objects in trays, bins, or on pallets, 3D sensors become a very important piece of the automation system, and here we'll show you why human-like 3D vision is essential.

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Challenge #1

Separate objects with similar shape

Zivid's industrial machine vision cameras output a full color point cloud, with pixel-wise XYZ + RGB data. This helps you detect and separate similar objects.

Challenge #2 

Recognize, inspect and categorize

Zivid's industrial 3D cameras provide 2.3 Mpixel point clouds, giving you the data output so there's no question about what objects you are looking at. To see how Zivid's 3D sensing compares against typical stereo vision, you can dive into stereo vs. structured light here.

Challenge #3

Separate objects for proper pick and place handling

Zivid's industrial 3D cameras with HD color RGB point clouds provide you the details and information needed for robots to work with humans, as humans. Learn more about the importance of detect, pick and place in the Zivid 2 page.

Challenge #4

Don’t miss the details

Don't miss details by using monochrome 3D scanners or TOF sensors. Zivid industrial 3D cameras provide high-definition, high dynamic range point clouds with full RGB colors. You can see examples of more product point clouds here.

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