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How Hyper built a 1M-accurate indoor GPS

andrewhart.me

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Re: How Hyper built a 1M-accurate indoor GPS

#51

Post by ndriscoll »

mpeg wrote: post #40
ndriscoll wrote: post #13

> They wanted to bring indoor maps and navigation to their retail stores... It turns out that this doesn’t just apply to retail. Every office, university campus, events venue, hotel, airport, warehouse, factory — basically everywhere indoors have some need to navigate people around, provide relevant information, and improve efficiency. You'd think they would add this information to openstreetmap then or at least put…

If you had read the article you would see: 1/ they need to collect ground truth data for their algorithm to work, it doesn't magically work everywhere. 2/ the ground truth data was collected mostly by their clients, it is not their data to give away for free I honestly don't see a problem with this technology, and I am a huge privacy advocate. First off, it uses the wifi signal strength + a model based on ground trut…

The criticism was directed at retailers. If they want to provide indoor maps... why not just do that? For my Target example, there's even a convenient place to put them in store: the posters that say to download an app to see a map. There's also a standard place where they can add their indoor maps for free without needing anyone's permission (openstreetmap). Or put them online with a public domain disclaimer and someone else will eventually probably do it.

Edit: In Target's case, they do apparently also put it on their website if you go hunting for it, but the ubiquitous pushing of apps is still annoying vs just putting it right there in the store as well, and perhaps offering a QR code + text link to the online version. They're clearly using it as bait to install their tracking/ads trojan. Also their online map for my store is east-west inverted for some reason (the east end of the building is on the left, the north on top), which would be immediately obvious if they mapped it to their building in OSM.

Re: How Hyper built a 1M-accurate indoor GPS

#52

Post by thijson »

mentar wrote: post #3

How does your system handle the massive variance in sensor quality (accelerometer, gyro, WiFi radio) between a high-end iPhone and a budget Android device? Does the 1m accuracy hold up across the board, or does it degrade gracefully? Getting this right seems critical for scaling to a 'billion people'

There's something called a Kalman filter:

https://en.wikipedia.org/wiki/Kalman_filter

It can combine several inaccurate sources and output a result that is more accurate than any one of them.

I was at an Amazon Fresh grocery store, and saw squares in the ceiling that look like QR codes. I guess that's how they are mapping the store.

Re: How Hyper built a 1M-accurate indoor GPS

#56

Post by skunkworker »

zokier wrote: post #49
ckrapu wrote: post #18

Do you think the basic physics and sensor tolerances would let you go to 10^-2 meters if the environment (e.g. wifi station placements, location of RF-interfering elements) was designed by you?

If you want high performance indoor positioning , look into 5G carrier phase positioning which claims cm-level accuracy. Fuse that with IMU and optical sensing and you certainly should get decent results

I've personally used one of these before in a performance where you needed individual based tracking, the chips were active UWB radios and sensors were placed and calibrated around the stage, I believe it was https://kinexon.com/products/kinexon-rtls was what was used.

Re: How Hyper built a 1M-accurate indoor GPS

#58

Post by chiph »

subhajeet2107 wrote: post #10

Why do you need a human to do the initial mapping ? why can't you use a Roomba or a smaller hardware for this task

Many times these stores have floor cleaning machines - either robotic or driven by a human. An employee could zip-tie their sensor to it, let it do its cleaning trip around the store, and return to collect the data later.

This would allow an employee to do several stores in a town in a single day. And potentially less chance of a workers-compensation claim being filed if they fall down while walking around looking at their device.

Re: How Hyper built a 1M-accurate indoor GPS

#59

Post by contingencies »

numpad0 wrote: post #45
mentar wrote: post #3

How does your system handle the massive variance in sensor quality (accelerometer, gyro, WiFi radio) between a high-end iPhone and a budget Android device? Does the 1m accuracy hold up across the board, or does it degrade gracefully? Getting this right seems critical for scaling to a 'billion people'

Peripheral chips aren't differentiated by user values like the end products that use them. You don't get more preciser sensors in high end phones. Everyone gets the same thing. You pay more only for more materials. Sensors that are actually a lot better than standard offerings would also be subject to and/ofs of ITAR or EAR or MTCR or local equivalents thereof, so progress in IMU appears to have been stagnating a bit…

This is the correct answer. They're all the same. The notion that Apple has some kind of edge here is farcical.

Re: How Hyper built a 1M-accurate indoor GPS

#60

Post by cyanydeez »

dimatura wrote: post #46

The few examples they show do look pretty good for a wifi-based method, although who knows how cherry-picked they are. I wonder how much the "SLAM" part is contributing and how sensitive that is to the sensor quality on the phone. I would've assumed that they'd be using vision, which seems to be the method of choice for other companies like niantic. The ground-truth data part for vision would certainly be more onerou…

He explains it fairly well if you understand how you'd go from wifi accuracy to SLAM. THE WIFI was providing 3m accuracy and the SLAM down to 1M. how much it provides is those two numbers. I'm sure the algorithms are complex but he points out that SLAM is corrected by the actual maps made by the self service app. So it's fairly easy to understand: the map provides a probability space, the wifi puts you within 3m and the SLAM is use to fill in the blanks with help from the probability space.
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