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Marco Polo: Finding a friend with only distance and motion

58 pointsby jackhogan11last Monday at 2:52 PM8 commentsview on HN

Comments

nickcwtoday at 7:42 AM

Great demo! Very interesting to see that if you wiggle the hunter (1) back and forth the accuracy improves.

I think this is expected but interesting to see as you see humans and animals doing exactly this to better gauge how far away something is.

The accuracy also improves (but not as much) if you wiggle the target (2) back and forth which I wasn't expecting.

jdhwosnhwtoday at 5:01 AM

Fyi the unscented Kalman filter is both easier to implement than the EKF, and also avoids several of the requirements that come along with the need to linearize (such as the differentiability requirement mentioned in the article). Also (to me, at least) the UKF is conceptually much cleaner, as the whole point is to place the approximation in the parameterization of the distribution, rather than on the function operating on that distribution.

https://groups.seas.harvard.edu/courses/cs281/papers/unscent...

stonlybtoday at 12:33 AM

Pilling on to say well done on the interactivity and visuals / design overall. I'm working to make producing posts like this universally accessible (http://motate.app/) and posts like yours are an inspiration.

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noentoday at 1:11 AM

Really well written article, thank you!

jmuxyesterday at 11:31 PM

nice work! the interactive visuals are really cool

treyclufftoday at 12:16 AM

I love a blog post with interaction

rayhanadevtoday at 1:57 AM

love seeing purdue hackers folks on hackernews :)

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