It’s a pretty cool system. Robots that do the full autonomous shebang, from SLAM to path planning to obstacle avoidance, are still quite rare. To do it all on a helicopter is just showing off.

One of the hardest things to get right was simply gathering the 1,000km dataset. The physical world is unforgiving! Everything breaks. I’ll have a few posts about the trials of building the data collection system over the next few days.

Whatever about the strange terminology, it’s great to see the research getting out there. It’s also nice to see the feedback from Robert Sim, who made a rather impressive vision-only robotic system with full autonomy a few years ago, still quite a rare accomplishment.

For anyone interested in the details of the system, have a look at my publications page. New Scientist’s description more or less resembles how our system works, but many of the specifics are a little wide of the mark. In particular, we’re not doing hierarchical clustering of visual words as the article describes – instead we learn a Bayesian network that captures the visual word co-occurrence statistics. This achieves a similar effect in that we implicitly learn about objects in the world, but with none of the hard decisions and awkward parameter tuning involved in clustering.

Update: Another new video, celebrating two successive days of autonomous runs.

There are a bunch of other worthwhile talks from the event. I particularly enjoyed Hugh Durrant-Whyte’s description of building a fully automated container terminal “without a graduate student in 1000km”.

When people first started using the internet, the idea of search engines didn’t need much promotion. People were very clearly lost, and needed some tool to find the interesting content. Adopting search engines was reactive, rather than active.

Visual search is not like that. If kooaba or others do succeed in building a tool that lets you snap a picture of any object or scene and get information, well, people may completely ignore it. They’re not lost – visual search is a useful extra, not a basic necessity. The technology may never reach usage levels seen by search engines. That said, it’s clearly very useful, and I can see it getting mass adoption. It’ll just need education and promotion. Shazam is great example of a non-essential search engine that’s very useful and massively popular.

So, promotion, and lots of it. What’s the best way? Well, most of the different mobile visual search startups are currently running trail campaigns involving competitions and magazine ads (for example this SnapTell campaign).  Revenue for the startups, plus free public education on how to use visual search. Not a bad deal, easy to see why all the companies are doing it. The only problem is that it may get the public thinking that visual search is only about cheap promotions, not useful for anything real. That would be terrible for long-term usage. I rather prefer kooaba’s demo based on movie posters – it reinforces a real use case, plus it’s got some potential for revenues too.

At the moment it only recognises movie posters. Basically it’s current form is more of a technology demo than something really useful. Plans are to expand to recognise other things like books, DVDs, etc. I think there’s huge potential for this stuff. Snap a movie poster, see the trailer or get the soundtrack. Snap a book cover, see the reviews on Amazon. Snap an ad in a magazine, buy the product. Snap a resturant, get reviews. Most of the real world becomes clickable. Everything is a link.

The technology is very scalable – The internals use an inverted index just like normal text search engines. In my own research I’m working with hundreds of thousands of images right now. It’s probably going to be possible to index a sizeable fraction of all the objects in the world -  literally take a picture of anything and get search results. The technology is certainly fast enough, though how the recognition rate will hold up with such large databases is currently unknown.

My only question is – where’s the buzz, and why has it taken them so long?

Update: I gave the app a spin today on a friend’s iPhone, and it basically works as advertised. It was rather slow though – maybe 5 seconds per search. I’m not sure if this was a network issue (though the iPhone had a WiFi connection), or maybe kooaba got more traffic today than they were expecting. The core algorithm is fast – easily less than 0.2 seconds (and even faster with the latest GPU-based feature detection).  I am sure the speed issue will be fixed soon. Recognition seemed fine, my friend’s first choice of movie was located no problem. A little internet sleuthing shows that they currently have 5363 movie posters in their database. Recognition shouldn’t be an issue until the database gets much larger.

The key thing PR2 is designed to support is mobile manipulation. Basically research robots right now come in two varieties – sensors on wheels, that move about but can’t interact with anything, and fixed robotic arms, that manipulate objects but are rooted in place. A few research groups have build mobile manipulation systems where the robot can both move about and interact with objects, but the barrier to entry here is really high. There’s a massive amount of infrastructure you need to get a decent mobile manipulation platform going – navigation, obstacle avoidance, grasping, cross-calibration, etc. As a result, there are very very few researchers in this area. This is a terrible shame, because there are all sorts of interesting possibilities opened up by having a robot that can both move and interact. Willow Garage’s PR2 is designed to fill the gap – an off-the-shelf system that provides basic mobile manipulation capabilities.

Brian: We have a set of demos that we hope that the robot can do out of the box. So things like basic navigation around the environment so that it doesn’t run into things and basic motion planning with the arms, basic identifying which is looking at an object and picking it out from sitting on the table and picking it up and moving it somewhere. So the idea is that it should have some basic mobile manipulation capabilities so that the researcher who’s interested in object recognition doesn’t have to touch the arm part in order to make the object recognizer better. The arm part is not to say that it can be improved but good enough.

If they can pull this off it’ll be great for robotics research. All the pieces don’t have to be perfect, just enough so that say a computer vision group could start exploring interactive visual learning without having to worry too much about arm kinematics, or a manipulation group could experiment on a mobile platform without having to write a SLAM engine.

Another interesting part of the interview was the discussion of software standards. Brian is one of the lead authors of Player/Stage, the most popular robot OS. Player is popular, but very far from universal – there are nearly as many robot OSes as there are robot research groups (e.g. CARMEN, Orca, MRPT, MOOS, Orocos, CLARAty, MS Robotics Studio, etc, etc). It seems PR2 will have yet another OS, for which there are no apologies:

I think it’s probably still too early in robotics to come up with a standard. I don’t think we have enough deployed systems that do real work to have a meaningful standard. Most of the complex robots we have are in research labs. A research lab is the first place we throw away a standard. They’re building the next thing. So in robotics labs, a standard will be of not much use. They are much more useful when you get to the commercialization side to build interoperable piece. And at that point we may want to talk about standards and I think it’s still a little early. Right now I’m much more interested in getting a large user community and large developer community. I’m less interested in whether it’s blessed as a standard by a standard’s body.

Anyone working in robotics will recognise the truth of this. Very much a sensible attitude for the moment.

More pictures of the car here, here and here.

The cars have two side-facing vertical scanners, and another forward-facing horizontal scanner. Presumably they will do scan matching with the horizontal laser, and use that to align the data from the side-facing lasers to get some 3D point clouds. Typical output will look like this (video shows data collected from a similar system built by one of my labmates.)

The other sensors on the pole seem to have been changed too. Gone are the Ladybug2 omnidirectional cameras used on the American and Australian vehicles, replaced by what looks like a custom camera array. This photo also shows a third sensor, which I can’t identify.

So, what is Google doing with 3D laser data? The obvious application is 3D reconstruction for Google Earth. Their current efforts to do this involve user-generated 3D models from Sketchup. They have quite a lot of contributed models, but there is only so far you can get with an approach like that. With an automated solution, they could go for blanket 3D coverage. For an idea of what the final output might look like, have a look at the work of Frueh and Zakhor at Berkeley. They combined aerial and ground based laser with photo data to create full 3D city models. I am not sure Google will go to quite this length, but it certainly looks like they’re made a start on collecting the street-level data. Valleywag claims Google are hiring 300 drivers for their European data gathering efforts, so they will soon be swimming in laser data.

Google aren’t alone in their 3D mapping efforts. Startup Earthmine has been working on this for a while, using a stereo-vision based approach (check out their slick video demonstrating the system). I also recently built a street-view car myself, to gather data for my PhD research. One way or another, it looks like online maps are headed to a new level in the near future.

Update:  Loads more sightings of these cars, all over the world. San Francisco, Oxford, all over Spain. Looks like this is a full-scale data gathering effort, rather than a small test project.