During the hackathon the team only did a simulated flight, not a real flight, so take the results on effectiveness with a grain of salt. In any environment with significant seasonal changes, localization based on google maps will be a lot harder.
Each 5 days, a satellite from sentinel mission take a picture of your location. It's 8 days for landsat mission. Those are publicly available data (I encourage everyone to use it for environment studies, I think any software people that care about the future should use it).
It's obviously not the same precision as the google map, and it needs update, but it's enough to take in account seasonal change and even brutal events (floods, war, fire, you name it).
Occurred to me that in a war or over the water this wouldn’t be useful. But I think it will be a useful technology (that to be fair likely already exists), in addition to highly accurate dead reckoning systems, when GPS is knocked out or unreliable, as secondary fall back navigation.
Seems like a really good use case for downtown anywhere, because otherwise the buildings make your gps go haywire and navigation sucks. Though I will say, and certain providers try not to admit this too loudly, a much better way to handle those downtown scenarios is to map out and keep track of the location of WiFi signals and use their relative strength as detected by your device to triangulate your position instead. Works really well in super dense areas like cities
IIRC many cruise missiles do a similar thing, except with radar and topographical maps instead of cameras and photos. Obviously the cruise missiles cost a hair more as well.
First generation Tomahawk did this (TERCOM) but later blocks acquired image matching guidance which is presumably today very advanced (I heard about it first around 1988).
The drone uses a camera mounted underneath it to position itself with imagery from Google Maps highlighting similarities in the images to get a rough estimate of the co-ordinates. Doesn’t Google Maps still require internet, you may ask?
Google Maps allows users to download segments of maps ahead of time, usually for use when you are travelling or camping out in remote areas. In this instance, the team used this feature to their advantage, allowing the drone to continue operating regardless of having a GPS satellite connection.
The entire point of such a build is to operate autonomously with local data in the presence of jamming | signal loss for other reasons.
You can download osm and satellite datasets for thousands of square miles in a few GB. If you need very high resolution satellite it's gonna be on the order of 1GB per ~10sqmi depending on what you're up to.
Not sure how they do it but I think it's quite feasible to extend a kalman filter to include camera intput referenced to an image. E.g. https://doi.org/10.3390/aerospace9090503
This article strongly reminded me of the "helicrane" system developed by NASA for the last Mars probe, which oriented itself by recognizing the terrain below it and using that to orient itself toward the right drop location: https://www-robotics.jpl.nasa.gov/what-we-do/flight-projects...
Fun stuff. Wonder if there is any requirements for how "bumpy" the terrain needs to be to get recognized properly by the system.
This is already being utilized in Ukraine since GPS guidance doesn't exist in large areas. I've also read that both Switchblade and Lancet drones have this ability.
The future of warfare does seem to be incredibly cheap autonomous (or nearly so) drones. I know Palmer Lucky's working on this, but his offering is still incredibly high priced...
Who's looking at this problem with the perspective that these should be 'ammo' and not 'aircraft'?
I wonder: if you can just measure the gyroscope readings and velocity, then knowing a starting coordinate, could you just calculate the current coordinate? What are the unknown parts of this equation which make a sattelite / other methodology a must?
The Kalman filter[2], which pops up on this site rather frequently, is often used for updating the current state.
The issue is that sensors are not perfect. Not just noise, but they might be slightly off in one direction more often compared to another for example. These errors accumulate, and so you can be quite off after not that long of a time.
You can only measure acceleration using an accelerometer. A gyroscope is technically still useful here for removing the gravity vector from the data accelerometer is reporting.
Your position would drift insanely fast. In a drone, gyro readings and velocity are being used in the ekf2 filter to calculate your position. But a GPS position is required to prevent longtime drift.
Obviously, in GPS deprived situations, you have to think about alternatives.
This approach is used very commonly - e.g. every airliner, submarine and military plane will have such a navigation system (augmented with GPS these days obviously).
The issue is that the system naturally drifts, so over time it will accumulate error which has to be mitigated somehow (e.g. in military equipment with finding a known landmark and fixing on it).
Yes, this is how navigation was done "in the past", without GPS, using inertial navigation systems. And pilots would recalibrate their position along the way or before arriving "on target" using known landmarks. The fancier systems could recalibrate using known star positions.
There are so many problems with this article. If an area is so "remote" it doesn't have GPS coverage, the Google Maps imagery quality is going to be garbage as well. It claims it works day or night but how does it work at night? Not only will detail from the camera be reduced but the available satellite imagery will be greatly reduced in detail as well. If the team has only conducted a simulated flight isn't the headline just plain wrong?
Maybe I missed it in the article but does the processing take place on the drone or at a control station?
How do you even print an object that large in 24hrs, nevermind actually testing/iterating on the design? It seems a little disingenuous to say those aspects were done in 24 hours unless I’m missing something.
It’s multiple parts so multiple printers, I’ve printed some plane parts from lightweight PLA and this seems pretty reasonable to me. Especially so if you imagine prior experience and multiple people. It would get tricky if you had to iterate the design though.
[+] [-] romanzubenko|2 years ago|reply
[+] [-] logtempo|2 years ago|reply
It's obviously not the same precision as the google map, and it needs update, but it's enough to take in account seasonal change and even brutal events (floods, war, fire, you name it).
[+] [-] fnordpiglet|2 years ago|reply
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[+] [-] meindnoch|2 years ago|reply
Without a GPS signal, I assume. Unless Google Maps is stored locally on their drone.
[+] [-] defrost|2 years ago|reply
[+] [-] AnarchismIsCool|2 years ago|reply
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[+] [-] JustARandomGuy|2 years ago|reply
Fun stuff. Wonder if there is any requirements for how "bumpy" the terrain needs to be to get recognized properly by the system.
[+] [-] ChumpGPT|2 years ago|reply
[+] [-] oneshtein|2 years ago|reply
[+] [-] gengelbro|2 years ago|reply
Who's looking at this problem with the perspective that these should be 'ammo' and not 'aircraft'?
[+] [-] unknown|2 years ago|reply
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[+] [-] MichaelMug|2 years ago|reply
[+] [-] gloosx|2 years ago|reply
[+] [-] magicalhippo|2 years ago|reply
The Kalman filter[2], which pops up on this site rather frequently, is often used for updating the current state.
The issue is that sensors are not perfect. Not just noise, but they might be slightly off in one direction more often compared to another for example. These errors accumulate, and so you can be quite off after not that long of a time.
[1]: https://en.wikipedia.org/wiki/Dead_reckoning
[2]: https://en.wikipedia.org/wiki/Kalman_filter
[+] [-] charcircuit|2 years ago|reply
measured acceleration = true acceleration + accelerarion error
velocity = (true acceleration)t + (accleration error)t + v_0 + v_0's error
position = (true acceleration)t^2/2 + (acceleration error)t^2/2 + (v_0)t + (v_0's error)t + p_0 + p_0's error
The error grows quadratically meaning that this equation can only be used for a brief amount of time.
[+] [-] dayjaby|2 years ago|reply
Obviously, in GPS deprived situations, you have to think about alternatives.
[+] [-] izacus|2 years ago|reply
The issue is that the system naturally drifts, so over time it will accumulate error which has to be mitigated somehow (e.g. in military equipment with finding a known landmark and fixing on it).
The so-called INS systems are quite the marvel of mechanical technology: https://en.wikipedia.org/wiki/Inertial_navigation_system
[+] [-] meindnoch|2 years ago|reply
When searching for "accelerometer navigation", the first result is https://en.wikipedia.org/wiki/Inertial_navigation_system
Don't be lazy.
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[+] [-] causality0|2 years ago|reply
Maybe I missed it in the article but does the processing take place on the drone or at a control station?
[+] [-] dna_polymerase|2 years ago|reply
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