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Just added indoor navigation to WeWork, without them knowing. Got this up and running in 2 days, using wifi + motion data for precise location. Every other indoor solution takes months and usually require beacons. Hyper is going to fix the indoor navigation space. 💪

Andrew Hart

28,069 subscribers

90,281 просмотров • 1 год назад •via X (Twitter)

Здоровье и велнес Наука и технологии

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Private livecam show

Комментарии: 11

Фото профиля Andrew Hart ᯅ

Andrew Hart ᯅ1 год назад

WeWork and others can sign up for free:

Фото профиля AndaSeat

AndaSeat1 год назад

🎨 Freelancer life is like breathing - sometimes fast, sometimes slow... 💫 X-Air Pro flows with your rhythm: 💨 Breathable mesh for those deadline sprints 💭 Adaptive tilt for brainstorming reclines 🔄 5D armrests for device-switching dance ⚡ C-shaped lumbar for your entrepreneurial backbone ✨ Freedom to move, space to create: 💝 Freelancer special: Create your comfort for $20 off! #FreelanceLife #CreateFromHome #WorkspaceGoals #CreativeLife 🎯💻

Фото профиля Tom Connole

Tom Connole1 год назад

Can you do the Toronto PATH please 🙏

Фото профиля Vine Layer 0

Vine Layer 01 год назад

Once the whole world is mapped in this way, we've got a new economy.

Фото профиля mr.tipton

mr.tipton1 год назад

This is so well done

Фото профиля 🟧Clean Coder🟧

🟧Clean Coder🟧1 год назад

Anything you can tell us about how you match up wifi to a physical location in the building? I assume you have to at least map the access points yeah? Really cool regardless.

Фото профиля Volodymyr

Volodymyr1 год назад

Airports needs this! Last week I was trying to navigate in @ParisAeroport and it was hard to get between 2G and 2E. Louvre another great application.

Фото профиля DAYWALKER

DAYWALKER1 год назад

Amazing. You are my favorite company / tech right now. So many applications, demo wows, natural growth path, and everyone wins. Retailer, customer, you. Good luck!

Фото профиля Bart Trzynadlowski

Bart Trzynadlowski1 год назад

Very slick!

Фото профиля MikeeBuilds ⛩️〰️🧱

MikeeBuilds ⛩️〰️🧱1 год назад

Definitely need to link up this @OhioState hospital with this.

Фото профиля Adam From Alaska

Adam From Alaska1 год назад

That is very clean. Excellent work

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33,817 просмотров • 1 год назад

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Once we started to work with large global retailers, we needed a better way to scale this process. Ideally, the staff at the store could do this themselves — rather than us flying our team across the world — and then we could lower the cost and timelines. So we built a self-serve version of our survey app, with a tutorial mode designed for beginners. Over time, we collected millions of data points, and so we were able to develop an algorithm which would auto-correct mistakes. In other words, if the surveyor accidentally placed their ground-truth location in the wrong place on the map, we could use our algorithms to detect it, and correct it. So now we have WiFi, and with and our efforts on producing a high quality survey, we have the best WiFi positioning available. With WiFi on its own, it’s achieving 3 meter accuracy. This is a great foundation to build on. WiFi + Motion data To refine this down to 1-meter accuracy, we realised that we could combine WiFi with the same technology behind self-driving cars and robotics: a motion system called SLAM (Simultaneous Localization and Mapping). SLAM uses the accelerometer, gyroscope and camera system to understand precise device motion. Imagine a car driving through a tunnel, using the motion since its last GPS ping to keep location accurate until it comes out the other side. On a phone, this technology is very reliable, and measures device motion with high precision. But SLAM is measuring motion within its own coordinate space, it’s not aligned with the real world. SLAM tracks the user’s relative motion, like “moved forward 2 meters, then turned left”, but does “forward” mean “north”, or some other direction? It’s not calibrated, so it could mean any location, any direction. We can’t rely on the compass to help us out with this, because phone compasses are notoriously incorrect — everyone knows the frustration of being sent the wrong way down a street. So our job was to align this motion data with the triangulation data we were receiving from WiFi. We designed an algorithm that could simulate every possibility, filter the unlikely scenarios, and hone in your location, using WiFi as an anchor. So WiFi gives us the initial blue dot, SLAM gives us motion, and as the user starts walking and we receive more data, our algorithms can refine location accuracy down to a consistent 1-meter accuracy. We’ve tested these algorithms in many locations, on hundreds of hours of ground-truth data:

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211,068 просмотров • 5 месяцев назад

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70,629 просмотров • 1 месяц назад

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NOF

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Wall Street Apes

262,127 просмотров • 14 дней назад

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As soon as the deal is approved, we'll start hiring small businesses. DC residents will start going to work. Next year, we'll put a shovel in the ground. And it will also get us sooner to our indoor SportsPlex for our young people. This morning on FOX 5 DC ⬇️

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Mayor Muriel Bowser

21,739 просмотров • 11 месяцев назад

a robot can recognize every object in a room and still not know how to open a single drawer knowing there's a cabinet is scene understanding. knowing which handle to grab, which direction to pull, and that it's a drawer not a door is affordance understanding almost no dataset has it in 3D SceneFun3D: 14,800+ functional elements across 710 laser-scanned indoor scenes. every handle, knob, button, and switch labeled with its affordance, motion axis, motion type, and a natural language task description each scene's 3D point cloud with its iPad video recordings in fiftyone. the affordance annotations live in 3D and project into the video frames check it out here

a robot can recognize every object in a room and still not know how to open a single drawer knowing there's a cabinet is scene understanding. knowing which handle to grab, which direction to pull, and that it's a drawer not a door is affordance understanding almost no dataset has it in 3D SceneFun3D: 14,800+ functional elements across 710 laser-scanned indoor scenes. every handle, knob, button, and switch labeled with its affordance, motion axis, motion type, and a natural language task description each scene's 3D point cloud with its iPad video recordings in fiftyone. the affordance annotations live in 3D and project into the video frames check it out here

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