Although image data storage isn't exactly small or cheap in terms of memory required, I think the basic idea here is analogous to that of machine vision image libraries, where the machine vision user builds up a database of images of objects to be inspected on the line, such as PC boards and components on the boards.
The idea is to create 3D scans of various objects to help teach robots about their environment and the objects in it, so they can navigate the environment and manipulate those objects, including, for example, refrigerators and people. An example given in this IEEE Spectrum article http://spectrum.ieee.org/automaton/robotics/robotics-hardware/kinecthome-wants-to-start-3d-scanning-the-world?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed:+IeeeSpectrum+(IEEE+Spectrum) is teaching a robot to open a fridge door. First, the robot has to have a map of a fridge door and how it operates. If the robot is Kinect-equipped, as many now are (in R&D, anyway), it can use 3D images for those maps. But fridges aren't all the same size, don't have the same kind of door, and doors aren't always located on the same side of the box. So it needs an image library for each object: lots and lots of images.
This is an interesting project. The Kinect is an interesting device, and has many uses. Building up a database in this way is an outstanding way to get a large mass of information in a short time. In AI it is very beneficial to have a large training set. Frankly, this is true of us humans as well.
Looks pretty cool and I like the crowdsourcing angle a ton, but I'm not really sure what kinds of scans are being collected with the Kinect. It is scans of people, physical objects, movements? I'm also curious how this data is being fed back to robotics designers for future use? My guess is through the site community, but just wanted to confirm.
In a bid to boost the viability of lithium-based electric car batteries, a team at Lawrence Berkeley National Laboratory has developed a chemistry that could possibly double an EV’s driving range while cutting its battery cost in half.
Using Siemens NX software, a team of engineering students from the University of Michigan built an electric vehicle and raced in the 2013 Bridgestone World Solar Challenge. One of those students blogged for Design News throughout the race.
Robots that walk have come a long way from simple barebones walking machines or pairs of legs without an upper body and head. Much of the research these days focuses on making more humanoid robots. But they are not all created equal.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.