Stereo vision with a twist

I’m agnostic when it comes to 3D machine vision. I see the value in acquiring another channel of data but I’m concerned about the complexity of processing it. So I read Winn Hardin’s “3D Vision Thinks Outside the Envelope”, (Visiononline.org, May 17^th, 2013,) very carefully.

I didn’t learn a whole lot – Cognex say their 3D camera and VisionPro tools are great, LMI think their Gocator is the bees-knees – but I did follow a link to the Ensenso N10 stereo 3D camera, sold by iDS.

After satisfying myself that it’s not a rebadged Point Grey Bumblebee, I wondered about the third port midway between the two camera lenses. Turns out, this is where they project a structured light pattern from. It’s the technique at the heart of “Projected Texture Stereo Vision” which is what makes stereo a useable tool.

Don’t feel bad if that phrase is unfamiliar to you, I hadn’t heard of it either, but Google has. A paper titled “Projected Texture Stereo” and put out by Willow Garage explains the whole thing. Math isn’t my strong suit, but the bottom line is straightforward: projecting a pattern onto an image provides the system with a whole lot more data from which to compute a disparity map. In other words, more data equals a better result.