Crave

The autonomous Audi TTS makes test runs on salt flats.

(Credit: Volksagen)

The Defense Advanced Research Projects Agency (DARPA) conducted its autonomous vehicle races, the Grand Challenge and the Urban Challenge, in 2005 and 2007, but Volkswagen is still researching the technology. A combined effort with Volkswagen's Electronics Research Laboratory (ERL), Stanford University's Dynamics Design Lab (SDDL), and Sun Microsystem's resulted in the autonomous Audi TTS. The group working on the car is considering a run up the 12.4-mile Pikes Peak Hill Climb course in 2010 to demonstrate the capabilities of its driverless technology.

This new robot car is based on a 2009 Audi TTS, the sport version of the TT coupe. This car has Quattro all-wheel-drive and is motivated by a turbocharged, direct injection, 2-liter, four-cylinder engine producing 265 horsepower and 258 pound-feet of torque. ERL fitted this car with the sensors, servos, and drive-by-wire equipment necessary for computer control, while SDDL developed the programming so the car can respond appropriately to sensor data. Sun built the computer platform to run the car.

Past autonomous cars from Volkswagen, developed by Stanford, have been a Touareg SUV and a Passat wagon. A video promoting the new Audi TTS shows the technology has progressed so that it can handle drifting and cross-turning the wheels, maneuvers necessary for any speedy run up Pike's Peak.

While this technology could lead to commutes and long freeway trips where you could sit back and let the car do the driving, the point of the current research is developing new safety technologies.

(Credit: Volkswagen Group of America)

If you're a person who would gladly relinquish the task of parking your car to a computer, there may be a Volkswagen in your future.

Last weekend, Volkswagen Group of America and Stanford University's School of Engineering hosted a dedication ceremony on the Stanford campus for the new Volkswagen Automotive Innovation Laboratory (VAIL) that included the "first ever" autonomous parking demonstration by a driverless car.

(Credit: Volkswagen Group of America)

The car, a VW Passat called Junior, was developed jointly by VW and Stanford and is the same one that finished second in the 2007 DARPA Urban Challenge. Driverless cars have come a long way since the first DARPA race in 2004, when not one contestant made it over the finish line, much less parallel-parked itself.

VW donated $5.75 million for the new laboratory, which it called "the next step in the evolution of the two organizations' commitment to drive innovation in automotive development."

"When the new building opens early next year, VAIL will provide a home on campus for faculty and students from around the university to work on advanced automotive research," said Jim Plummer, dean of the Stanford School of Engineering.

The company also unveiled the Pike's Peak Audi TT-S, the latest iteration of driverless vehicles developed through the VW-Stanford partnership.

(Credit: Massachusetts Institute of Technology)

A group of engineering students has developed a portable, low-cost Braille printer that lets the blind and visually impaired easily label objects that feel similar to the touch, like DVDs.

The 6dot Braille Labelmaker is cheaper and easier to use than other label makers, according to the students from MIT, the University of Toronto, and Stanford University. Still in development, it might cost as little as $200 when released, less than half the cost of some other Braille label makers.

Users push six keys on the ergonomic device--one for each of the dots that make up a Braille character--to emboss any character, including contractions, on a roll of adhesive tape. An internal microprocessor can store up to 16 characters.

The input method is similar to standard Braille typewriters, but since the 6dot runs on two AA batteries, the print quality is high, the team says.

Visually impaired people often print Braille labels to identify objects like cans, medicine bottles, and AC adapters. The developers say 6dot will meet a need for labeling things on the go, and can also be used to help blind children learn Braille.

(Credit: Boston Dynamics)

Here's another offering from Boston Dynamics' zoomorphic line: the RiSE V3, a multi-legged, beaver-tailed robot that can skitter along the ground, shimmy up a pole, and then quietly cling there and stare at you.

The legs are powered by a pair of electric motors and equipped with small surgical needle micro-claws, which allow the unit to dig into and climb up textured, convex, cylindrical structures at a rate of 21 centimeters per second, or just under a half a mile an hour (PDF).

"RiSE V3 is the first general-purpose legged machine to achieve this vertical climbing speed," said University of Pennsylvania Professor Daniel Koditschek, who worked on the project.

The RiSE was the result of a collaboration between Boston Dynamics, the University of Pennsylvania, Carnegie Mellon, U.C. Berkeley, Stanford, and Lewis and Clark University, with funding by DARPA.

As with the company's now famous BigDog, what distinguishes this robotic creation is its freakishly familiar gait. RiSE uses a distinctive, koala-like climbing pace, or behavioral gait, propelling the body forward while passively maintaining yaw, pitch, and roll stability. Locomotion--leg motion, strain, and joint position and foot contact sensors--is controlled by an onboard computer, naturally. The front legs are just long enough to hug a telephone pole.

The development team's aim was to reproduce movements they had observed in climbing insects. This is something else that sets this wall climber apart. Most other climbing robots have generally relied on "surface-specific attachment mechanisms," i.e. magnets and suction devices.

Watch a video of RiSE V3 below.

A correction was made to this story. Read below for details.

Want some real-time desktop video-conferencing and application sharing? You got it, circa 1968. Click on the above photo to see more images from Tuesday's demonstration recounting the groundbreaking Stanford work that led to the mouse and more.

(Credit: Scott Ard/CNET Networks)

Forty years ago this week, Doug Engelbart and fellow researchers at the Stanford Research Institute (SRI) gave a demonstration in San Francisco that turned out to be a prescient vision of the future: it included a computer mouse; hypertext linking; real-time, on-screen text editing; shared-screen teleconferencing; and other revolutionary ideas.

Tuesday, a large crowd gathered at Stanford to watch a video of the historic demo and hear original team members recount their groundbreaking work. Click here to see photos from the presentation.

Correction: This story initially misspelled the primary researcher's name. It is Douglas Engelbart.

Stanford scientists gloat over their incredible accomplishment and how insignificant my own have been...maybe.

(Credit: Stanford News Service)

On Monday, a 4-foot-long helicopter equipped with an artificial-intelligence system developed by computer scientists at Stanford University flew over the campus, according to Reuters. The helicopter had taught itself to fly by watching the aerobatics of a radio-controlled helicopter flown by a human. Also on Monday, I got my Warlock to level 66 in World of Warcraft. Yeah, I think I need to reevaluate my priorities.

The self-controlled mini-hopper performed flips, rolls, pirouettes, stall-turns, knife-edges, and an inverted hover over a field. Adam Coates, a Ph.D. student who worked on the project, said the machines can fly maneuvers at the edge of what real helicopters are capable of.

Each helicopter costs about $4,000 to build. A number of companies that are hoping to use the A.I. for surveillance and mapping applications have contacted the research group.

Check out a couple of videos here.

It's one of the oldest, most common problems in photography: that picture you thought would be the prize shot is out of focus.

Refocus Imaging, a Silicon Valley start-up, thinks its technology can be used to make cameras that can fix that problem--after you take the photo.

By fitting a camera's image sensor with a special lens and then processing the resulting data with new methods, Refocus Imaging's technology will let photographers fix their photos and exercise new creative control after the shutter is released, founder and Chief Executive Ren Ng said.

"There's a lot of physical stuff in the camera that is limiting its performance," Ng said. "What we're doing is to capture much more than a two-dimensional photograph inside the camera...By collecting the light, we can process it in software to do what the hardware usually has to do."

And the technology boosts some aspects of camera performance in the process, he said. Ng said he hopes to license it to camera companies, and boasts that Refocus Imaging's patent portfolio is "very, very good."

The technology, which stems from Ng's research at Stanford University, is an example of computational photography, which augments traditional image capture with computers--either in the camera or on a PC--to achieve new possibilities.

Included here are examples from Refocus Imaging that show how the technology works. The slider on the right of each graphic can be used to change the point of focus from foreground elements to those in the background, or clicking on a different area will bring it into focus.

Ng also showed the technology off at the 6sight digital-imaging conference in November.

The way Ng sees it, the Refocus Imaging technique has several possible advantages. For one thing, being able to focus images after the fact means that cameras could take a picture sooner without waiting for an autofocus mechanism to lock in. For another, because the depth of field also is adjustable along with focus, a pro photographer could fine-tune a picture to properly blur a background or get just the right amount of a subject in focus.

Refocus Imaging CEO Ren Ng

(Credit: Refocus Imaging)

"One way to think of it is just a raw image, except to the nth degree," Ng said, referring to the raw images that higher-end cameras can record directly from the image sensor, leaving processing choices to the photographer. "It contains a ton more information than a raw picture today. There are all kinds of creative controls you couldn't even conceive of now."

Another advantage is that the technology works better in low light, he said. And by transforming the light's optical properties using a computer instead of relying just on the camera's lenses, a computing system can correct aberrations to improve lens sharpness, as well as heighten lens contrast and lower its manufacturing costs.

Sounds swell, right? Well, there's still no thing as a free lunch.

A lot more image processing is required, for one thing, though Ng legitimately points out that camera processors are steadily improving. Another big drawback is that the full resolution of the image sensor isn't available in the ultimate image the camera produces.

Ng isn't willing to discuss exactly how much resolution is lost in the process at this stage in the company's research. "You can get gorgeous 4x6 prints or (larger), and take those much more dependably," he said.

Refocus Imaging's ideas are related to an image sensor that can see in 3D, in a sense, that another Stanford researcher, Keith Fife, demonstrated earlier this month. That sensor also uses tiny lenses, but his are built directly into the sensor, with one lens dedicated to a particular subarray of sensor pixels.

Ng's company is one of several organizations researching the idea of the "light field," which describes all the light entering a camera, not just the subset that gets photographed with a particular camera setting. Ng offers an analogy: where a photograph is like an X-ray image, the light field is like a three-dimensional CT scan that lets a doctor effectively look at the interior of a person from any direction.

One light field research project at Stanford in the 1990s used an array of 100 cameras all taking a photo of the same subject, then compressing the resulting image data into a representation of the light field. With Refocus Imaging's technology, Ng said, "we can make that compression in a single device."

Most folks think of a photo as a two-dimensional representation of a scene. Stanford University researchers, however, have created an image sensor that also can judge the distance of subjects within a snapshot.

To accomplish the feat, Keith Fife and his colleagues have developed technology called a multi-aperture image sensor that sees things differently than the light detectors used in ordinary digital cameras.

Each subarray on the multi-aperture sensor captures a small portion of the overall image, a portion that overlaps slightly with that of the neighboring subarrays. By comparing the differences, a camera can judge the distance of elements in the subject. (Note that this mock-up differs from reality, in which each subimage would be rotated 180 degrees, but this makes the idea easier to grasp.)

(Credit: Keith Fife/Stanford University)

Instead of devoting the entire sensor for one big representation of the image, Fife's 3-megapixel sensor prototype breaks the scene up into many small, slightly overlapping 16x16-pixel patches called subarrays. Each subarray has its own lens to view the world--thus the term multi-aperture.

After a photo is taken, image-processing software then analyzes the slight location differences for the same element appearing in different patches--for example, where a spot on a subject's shirt is relative to the wallpaper behind it. These differences from one subarray to the next can be used to deduce the distance of the shirt and the wall.

"In addition to the two-dimensional image, we can simultaneously capture depth info from the scene," Fife said when describing the technology in a talk at the International Solid State Circuits Conference earlier this month in San Francisco.

The result is a photo accompanied by a "depth map" that not only describes each pixel's red, blue, and green light components but also how far away the pixel is. Right now, the Stanford researchers have no specific file format for the data, but the depth information can be attached to a JPEG as accompanying metadata, Fife said.

Recording photos in three dimensions is a pretty radical overhaul of the concept. Depending on your preferences, it could be anything from an exciting new frontier to the latest annoying digital gimmick.

Either way, you'd best start thinking about the implications because Fife isn't the only one working on the challenge. Image-editing powerhouse Adobe Systems has shown off some 3D camera technology too. It should be noted, of course, that stereoscopy itself is an old and respected photographic subject.

Even if you don't want to print holographic pictures of your new kitten, I suspect that 3D technology could help with some traditional photography challenges. Just as face detection can make a camera decide better where to focus and how to expose a shot, having a depth map could make this sort of calculation that much more sophisticated.

This diagram shows the multi-aperture sensor, which puts a small lens over a group of image sensor pixels. Each subarray gets its own microlens.

(Credit: Keith Fife/Stanford University)

Other advantages
Depth isn't the only potential advantage of the multi-aperture approach, Fife said. It could also help reduce noise, which in digital photography takes the form of colored speckles that are a particular plague when shooting at higher ISO sensitivity settings.

The noise is reduced because multiple subarrays capture the same views. It's therefore easier to distinguish true color of the subject from off-color noise. In addition, each subarray can be set to record a specific color, which could reduce the "color crosstalk" of current image sensors, he said. Today's "Bayer" pattern sensors employ a checkerboard of red, green, and blue pixel sensors, but bright red light captured by a red pixel can, for example, leak out a bit and affect the neighboring blue and green pixels.

Each subarray gets its own microlens. Although that complicates the manufacturing of the sensor, it could simplify the lenses used in existing cameras, Fife said. And lens manufacturing today certainly has no shortage of difficulties with a variety of exotic glass and even fluorite crystal elements, aspherical elements, and other avant-garde optics.

"There is opportunity for most of the complexity of the lens design to sit at the semiconductor rather than at the objective lens," Fife said. "Although the local optics (on the sensor) may be challenging, it is possible that the optics can be better controlled with lithography and semiconductor processes than with the injection molding and grinding that is used in the conventional camera lenses."

The microlenses might even be all that's needed for some applications, such as taking super-closeup "in vivo" photos inside plant and animal subjects where there's no room for a camera, Fife said. "The multiaperture sensor can form images at close proximity...because no objective lens is needed," Fife said.

This photo shows the prototype chip with 12,616 subarrays. Each pixel on the chip is 0.7 microns on edge, and the chip consumes 10.45 milliwatts of power.

(Credit: Keith Fife/Stanford University)

No free lunch
Lest you get carried away by the technology, you should be aware of a number of caveats:

• Because the same subject matter is captured redundantly by multiple pixels, the ultimate sensor resolution is lower than the raw number on the overall sensor.

• Processing the image, both to figure out how to merge the subimages into one overall image and to create the depth map, takes about 10 times as much processing horsepower as conventional on-chip image processing. Cameras already are battery hogs, and nobody wants to draw any more power or slow down camera performance.

• 3D images are possible only with subjects that have texture and other detail. "If a picture is captured of a perfectly smooth white wall, it is impossible to estimate the distance to that wall," Fife said.

So those are the downsides, but that's par for the course with new technology. And even if the technology never materializes, it's a strong indicator of the radical transformations that are in store for digital photography.

Click the image to watch the videos

The 2007 DARPA Urban Challenge represents a new frontier in autonomous vehicle technology. We were live onsite for the final of this year's event, which saw 11 robot cars compete in a six-hour contest in a simulated urban environment, complete with traffic, intersections, and parking lots. The rolling robots varied from a driverless 12-ton Oshkosh truck to an autonomous Toyota Prius. Check out our video diary from this weekend's event.