Here's a few thoughts:
* They say it has a 95 degrees vertical FOV and 360 degrees FOV. The vertical FOV is awesome actually.
A Velodyne VLP-16 has roughly 30 degrees FOV. We need the vertical and horizontal angular resolution to make any first-take judgements though.
* One overlooked feature I want to point out is that the Minimum Range is 0. Many lidars perform well at far distances but struggle to see objects very close (<1 meter away). This poses a safety issue as someone or something is effectively invisible to the robot if they're too close. Of course you can use cameras to handle this case but this is a big win for the Waymo hardware team!
* They're going to be 15k+ (gut estimate)
By the way, if you want to to become an autonomous vehicle engineer I put together a resource guide here: https://becomeautonomous.com
Any idea about its price ?
This announcement reads like a 'Plan B' for Waymo's lidar team. Waymo's talk of buying 20k Jaguar Ipaces and 60k Chrysler Pacificas for robotaxi deployment is indefinitely on hold, and if that was playing out the way they wanted it to, then Waymo's lidar production team would have more important things to do than fishing around for a secondary Lidar market to sell their wares to.
When it was first announced, and the research paper was published showing how it was built and functioned (essentially a 2D spinning parallax distance measuring system using a laser and linear photosensor array), it was thought that the system would be very cheap to make, and would eventually sell to hobbyists for around $25.00.
Unfortunately - that never happened.
I don't know what it ultimately cost Neato to make; it was likely inexpensive in quantity, but it was never sold to hobbyists or robotics researchers as a standalone product. Instead, if you wanted one, you had to either purchase and cannibalize one of the robots, or otherwise obtain one as a replacement or "pulled" component. Even then you would spend much more than $25.00 to get one.
Eventually they started to show up on Ebay; at one point I was able to purchase the units for around $35.00 at the cheapest; these were "used pulls", some needed new motors, pulleys, and/or drive belts (all parts easily found as well) - so that pushed the price up a bit.
Lately, the prices on Ebay have settled to around $75-90.00 for refurbished pulls, or replacement new units.
Interestingly, similar units have also appeared on "Chinese clone" vacuum robots, and you can purchase the LIDAR units they use separately on sites like Alibaba, etc. But even there they don't sell for anywhere near $25.00 each.
I am not sure where the discrepancy for this device lies; maybe the quantity just isn't there, and honestly $100.00 for a 2D LIDAR (granted, indoor only and very limited range) is fairly inexpensive, but even so I don't see many people using them on their hobbyist devices. Most research robotics continue to use the more expensive SICK and Hokuyo 2D LIDAR (which are time-of-flight based).
I think it is more likely that if LIDAR is used, it will be part of a large suite of sensors, and likely 2D only, unless the price of flash depth LIDAR devices plummet.
I'm not an expert, but I personally think that computer vision based approaches can work well, especially combined with other sensor systems (LIDAR, RADAR, ultrasonics, etc). NVidia has shown that vision-based approaches can be made to work, and others have done similar work that show the approach to be viable. I'm a bit biased on this because I used a simplified version of NVidia's approach to pilot a simulated vehicle in an online MOOC I was a part of a few years back (I had to simplify the CNN to make the model fit the memory I had in my GPU at the time; I had tried to use a batch training method but wasn't able to get it to work properly before my deadline - even so, the simplified model trained and generalized quite well with the data I gathered).
They are explicitly limiting this to use cases that don't compete with self-driving, so it is likely bound by licensing and non-compete agreements.
So don't expect seeing one of these sensors in a competing self-driving vehicle any time soon.
Lately I've seen used Velodyne 3D LIDAR units being sold on the site for (compared to new) dirt cheap. Granted, much more expensive than used SICK or Hokuyo 2D sensors - but that would be expected.
The fact that they are being sold on Ebay at all is a fairly new and interesting development; the question becomes "where are they coming from"? That is, what is the secondhand source they are being pulled from.
In the case of SICK and Hokuyo sensors, it has generally been industry or research. For Velodyne sensors, they were explicitly made for self-driving vehicles (though they've found other markets since).
It makes me wonder if one or more the established self-driving vehicle companies are shedding the tech in favor of some other system...?
It'd be interesting to hear from someone who knows specifics of LIDAR manufacturing, though.
Guesstimate the build cost + total solar input per year. Get instant payback number.
Use LIDAR to establish some sort of canonical clothes size format? Not just S, M, L, XL that only works for your region. Maybe you don't even need LIDAR, but I'd love personalised clothing.
If it's cheaper than thermal cameras - perhaps some use for lifeguards, especially in night?
You could prototype the solar calculator yourself with any modern mobile device in a trivial amount of time.
For video games maybe but I'd always try to avoid looking at those, even if LiDARs are eye safe it's not fun staring at them.
Alternately, a cheap way to produce amazingly accurate video game maps of your neighborhood.
Much cheaper cameras may be fine for that though...
It will likely come with licensing and non-compete agreements.
That's simply not true. If framework X supports <client that connects to complex hosted service Y> then "using it in my home PC" isn't automatically possible.
I've heard Velodyne has a new solid state array in the works, but no idea how it compares to something like this. At CES this year there were quite a few vendors with various flash LIDAR tech.
A Velodyne VLP-16 has roughly 30 degrees FOV. We need the vertical and horizontal angular resolution to make any real judgements though.
"Select partners" can mean "those who won't compete with us".
And when it's a monopoly that's already repeatedly demonstrated monopolist behavior, it's even less permissible. So they have a patent monopoly on the hardware, already shown they'll fight in court to protect it (re: Uber), and will prevent anyone from buying that hardware if it's a potential competitor to their service-based business. Their choice to sell the hardware makes it a separate market than their ride-sharing service, and one could argue a violation of antitrust law if they're blocking competitors from one market from buying their product in another.
Unless you're actually trying to argue that "warehouse lidar produced by Google" is a meaningfully different market than "warehouse lidar", which completely contravenes both written law and precedent.
Also the right of first sale is trademark/copyright law, not contract law. Companies are absolutely allowed to create non-trademark agreements, and they do all the time. Think software licenses.
And apparently patent law, you can't use your patents to force someone who legally acquired a device to not do something with it.
So, if one legally got their hands on one of these things there's not a thing waymo could do to them if they were "misusing" it.
I assume, of course, you were likely trying to make some sort of "iVerge" claim here, but the reality is, The Verge gives Google incredibly favorable press coverage as well. At the end of the day, The Verge is a business that's in it for clicks, and exclusive interviews and insider access equals clicks. So The Verge will shill for just about any company that can give them scoops, including both Apple and Google. Both companies are notoriously brutal to sites that write critically about them, and The Verge avoids heavily criticizing either in most cases, as to not jeopardize their access.