Marco Polo | Jack Hogan
jackhogan.me · 2,837 words · saved by 1 readers
Finding a friend with only distance and motion.
You walk into a cafe, looking for your friend. Seems like an easy task, until you see it’s so packed that you can’t see through the crowd at all, and everyone’s talking so loud that you can barely hear anything. The only things you know are your movements, and how far you are from your friend (through the special psychic bond you two share). How will you find each other? I wanted to solve the exact same problem, but with devices instead of people (so no psychic connection for me), existing in a space of hundreds of other devices. Working the problem taught me a lot of really interesting…
related reading
- Kalman filter - Wikipediaen.wikipedia.org
- ProbabilisticRobotics.pdfdocs.ufpr.br
- GPS – Bartosz Ciechanowskiciechanow.ski
- How a Kalman filter works, in pictures | Bzargbzarg.com
- 23_ese650.pdfpratikac.github.io
- Pedestrian Dead-Reckoning (PDR) – An Introductionadvancednavigation.com
- arxiv.org/pdf/2101.09515arxiv.org
- The Bayes Filter and Intro to State Estimation | John Lambertjohnwlambert.github.io
- olson2011tags.pdfapril.eecs.umich.edu
- The math behind Extended Kalman Filtering | by Sasha Przybylski | Mediummedium.com
- wivi-paper.pdfpeople.csail.mit.edu
- Uniquely Localizable Networks with Few Anchors | Springer Nature Linklink.springer.com