Hacker News Reader

Comment by foota | Hacker News Reader

You could probably trade accuracy of route position for accuracy of distance by averaging samples.

You can see how off the trace can be here: https://i.imgur.com/eHJI8FH.jpg . You could probably do some smoothing to help fit some of this to a street but I've been on runs where I zig zag down streets just to add distance or to do hills.

Wow, that's pretty horrible, must be giving you close to twice the actual distance. I think Google Maps on my phone applies some smoothing that does this a lot better, but then it can lag for a while when I'm going sharp right in an intersection.

Maybe try telling it that you're biking instead, my guess is they do more smoothing the higher speed they expect.

It's pretty much raw GPS if you're in walking or biking mode. Driving mode uses sophisticated filtering algorithms, and doesn't actually make any assumptions about your speed, though it does assume you're on a road.

Here's a look at some of my biking in Midtown Manhattan (iPhone 7 / Strava). It's pretty much garbage.

https://imgur.com/mJFFKoV

https://imgur.com/RYuYrcI

I've been working on GPS filtering for three years and that's bad even by my standards.

Chips with L5 should do a lot to clear that up though.

I have a question for you. If GPS locations in cities are affected by reflections off buildings, wouldn't the effect be deterministic and could you take advantage of that to reduce the noise?

It's deterministic, but changes multiple times per city block, so it's hard to build a model that accurately does something about it.

Yes, this is true! If you are interested in working on this kind of thing, please reach out to me via HN messaging.

Reply on news.ycombinator.com