Kinect@Home Wants to Start 3D Scanning the World

Big data would be a big help to robots, so let's go out there and get it with the help of Kinect

2 min read
Kinect@Home Wants to Start 3D Scanning the World

Back in January, Adept's Erin Rapacki told us all that it's time to start 3D scanning the world. We agree with her, but it's not an easy thing to actually go and, you know, do. There are approximately 975 bajillion different objects out there in the world that robots need to know how to interact with, and the only way we're going to learn about them all (short of Google throwing approximately 975 bajillion dollars at the problem) is through a cooperative, crowdsourced effort like this new project called Kinect@Home.

What Kinect@Home wants to do is to harness the power of all of those Kinects that roboticists and gamers have lying around out there and put them to work recording 3D models of, uh, pretty much everything. Here's how it works:

  • You download some drivers and a plugin from the Kinect@Home website.
  • Plug your Kinect into your computer.
  • Run the plugin, and move your Kinect slowly around whatever it is that you want to create a model of.
  • The data gets uploaded and rendered in the cloud, and you get back a nifty browsable, embeddable 3D model like this:

 

If you want to get crazy and download the model itself and mess with it, you can do that too. Cool!

The whole point of this is not really to give you a nifty new 3D modeling tool. Rather, Kinect@Home is hoping that people will make scans of every single last one of those 975 bajillion different objects that exist, which Kinect@Home can then analyze, categorize, and use to create better computer vision algorithms. From a more practical standpoint, we're talking about teaching robots to be better at navigating environments and manipulating objects.

For example, let's say that we want to teach a robot to open a refrigerator. To do that, a robot first has to recognize a refrigerator, but there are all kinds of different refrigerators and we have no idea what particular sort our robot is going to be asked to deal with. With a Kinect@Home dataset, it might be possible to go check out models of thousands of refrigerators in people's homes, and use those models to teach our robot how to locate (and even open) a generalized fridge. And once the dataset exists, there are all kinds of other things that we could do with it too, from object recognition to semantic mapping to localization to scene comprehension. 

Kinect@Home is totally, completely free, and you can download it at the link below.

[ Kinect@Home ]

Thanks Alper!

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Robot with threads near a fallen branch

RoMan, the Army Research Laboratory's robotic manipulator, considers the best way to grasp and move a tree branch at the Adelphi Laboratory Center, in Maryland.

Evan Ackerman
LightGreen

This article is part of our special report on AI, “The Great AI Reckoning.

"I should probably not be standing this close," I think to myself, as the robot slowly approaches a large tree branch on the floor in front of me. It's not the size of the branch that makes me nervous—it's that the robot is operating autonomously, and that while I know what it's supposed to do, I'm not entirely sure what it will do. If everything works the way the roboticists at the U.S. Army Research Laboratory (ARL) in Adelphi, Md., expect, the robot will identify the branch, grasp it, and drag it out of the way. These folks know what they're doing, but I've spent enough time around robots that I take a small step backwards anyway.

The robot, named RoMan, for Robotic Manipulator, is about the size of a large lawn mower, with a tracked base that helps it handle most kinds of terrain. At the front, it has a squat torso equipped with cameras and depth sensors, as well as a pair of arms that were harvested from a prototype disaster-response robot originally developed at NASA's Jet Propulsion Laboratory for a DARPA robotics competition. RoMan's job today is roadway clearing, a multistep task that ARL wants the robot to complete as autonomously as possible. Instead of instructing the robot to grasp specific objects in specific ways and move them to specific places, the operators tell RoMan to "go clear a path." It's then up to the robot to make all the decisions necessary to achieve that objective.

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