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Check out this drone: Joshua Bird built this drone ($20) in his dorm! [open-source motion capture system⬇️] Built at low cost, a motion capture system for tracking & and flying drones autonomously, with millimeter-level precision at room-scale. The student used $1 PS3 Eye cameras with 150fps capability. The challenge?... show more

Ilir Aliu

53,800 subscribers

101,689 просмотров • 1 год назад •via X (Twitter)

Наука и технологии Образование

Anya Rossi• Live Now

Private livecam show

Комментарии: 10

Фото профиля Bruno

Bruno1 год назад

@PalmerLuckey cc

Фото профиля ARK Electronics

ARK Electronics2 лет назад

Elevate your drone game with our USA-made, NDAA-compliant flight controllers! Trust in reliable technology that not only enhances your flights but also supports US drone manufacturing capability. 🇺🇸✈️ #USAMade #NDAA #drones #uav #uas #px4 #ardupilot #USA #unmanned #opensource

Фото профиля Ram

Ram1 год назад

@CaptVenk future project for junior

Фото профиля QVelard

QVelard1 год назад

Very cool

Фото профиля Sam Hiderman VI (e/acc)

Sam Hiderman VI (e/acc)1 год назад

How much to make place propeller guards on the drone

Фото профиля ChartChaser

ChartChaser1 год назад

I've never seen a European tech unicorn built without American VC money. -@IlirAliu_ Thanks for sharing Ilir (Free Man). Përshëndetje nga Shkupi.

Фото профиля sai vivekanand reddy

sai vivekanand reddy1 год назад

Cool 👌

Фото профиля Vindex

Vindex1 год назад

Really awesome!!

Фото профиля Thomas

Thomas1 год назад

What’s the benefit of this over using internal distance sensors?

Фото профиля DrReefer

DrReefer1 год назад

Really cool

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Circuit

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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:

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:

Andrew Hart

90,946 просмотров • 11 месяцев назад

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We built an AI-steered homing/killer drone in just a few hours I thought it would be fun to build a drone that chases you around as a game. It just uses an AI object detection model to find people in the frame, and then the drone is programmed to fly towards this at full speed as soon as it detects someone. This literally took just a few hours to build, and made me realize how scary it is. You could easily strap a small amount of explosives on these and let 100's of them fly around. We check for bombs and guns but THERE ARE NO ANTI-DRONE SYSTEMS FOR BIG EVENTS & PUBLIC SPACES YET. I was also able to add face recognition to it, and only make it attack someone it knew who was, it could easily identify the person from 10 meters distance. My bet is that we will see some sort of terror attack using this type of tech within the next few years. You still need some technical knowledge to build this now, but it becomes easier and easier. We need to build anti-drone systems for civilian spaces asap. I'm usually an open-source absolutist, but I will not post for now, it's honestly super easy to code but no point enabling. Disclaimer: this was built on a tiny drone for a game and would not hurt anyone, just wanted to spread awareness around this .

Luis Wenus

549,048 просмотров • 2 лет назад

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Black Eye 2.0 is live. We’re helping make Unreal Engine an incredible place to work with cameras: gameplay, cinematics, mocap, previs, live events, trailer capture, and anywhere else cameras need to be great. Black Eye 2.0 includes our new gameplay camera system, built to transform how teams create, tune, blend, prioritize, and manage gameplay cameras in UE. It's available on Fab It also includes the new Black Eye Panel, which makes camera creation and iteration absurdly fast and much more accurate. You work through the lens, like a DP. We’ll be at Unreal Fest Chicago, with a live demo in Room E258 on Wednesday the 17th from 1–2pm. Come see Black Eye 2.0 in action, ask questions, hang out, and talk cameras. Link to the full v2 breakdown video below.

Adam Myhill

19,106 просмотров • 1 месяц назад

#WATCH | A new low-cost Counter Drone System in Hard Kill mode 'Bhargavastra', has been designed and developed by Solar Defence and Aerospace Limited (SDAL), signifying a substantial leap in countering the escalating threat of drone swarms. The micro rockets used in this counter-drone system underwent rigorous testing at the Seaward Firing Range, Gopalpur, achieving all designated objectives. Three trials were conducted for the rocket at Gopalpur on 13th May 2025 in presence of senior officials of Indian Army. Two trials were conducted by firing one rocket each. One trial was conducted by firing two rockets in salvo mode within 2 seconds. All four rockets performed as expected and achieved the required launch parameters underscoring its pioneering technology in mitigating large-scale drone attacks: Solar Defence and Aerospace Limited. (Video Source: Solar Defence)

#WATCH | A new low-cost Counter Drone System in Hard Kill mode 'Bhargavastra', has been designed and developed by Solar Defence and Aerospace Limited (SDAL), signifying a substantial leap in countering the escalating threat of drone swarms. The micro rockets used in this counter-drone system underwent rigorous testing at the Seaward Firing Range, Gopalpur, achieving all designated objectives. Three trials were conducted for the rocket at Gopalpur on 13th May 2025 in presence of senior officials of Indian Army. Two trials were conducted by firing one rocket each. One trial was conducted by firing two rockets in salvo mode within 2 seconds. All four rockets performed as expected and achieved the required launch parameters underscoring its pioneering technology in mitigating large-scale drone attacks: Solar Defence and Aerospace Limited. (Video Source: Solar Defence)

ANI

321,007 просмотров • 1 год назад

🚨🇨🇳🇨🇳Here are two of China's Top tech companies, BYD, the worlds leading NEV manufacturer, and DJI, the worlds leading drone maker collaborating to push technology to a new level. Last night I went to the press launch of BYD's Lingyuan Drone system, which is a drone pod on top of your car. It will initially be available on Boa-5, Bao-8, Yangwang U8 & N9, Tang L DM, Sea Lion o7 DM-i. It can return to the cabin from 2Km away, and can follow the vehicle at uptown 54Km/h, with three centimetre obstacle avoidance accuracy. It can be launched with one press of a button and can follow the vehicle without any additional user control. The drone is a slightly modified version of the DJI Mavic Air 3S which has lidar obstacle detection, and also comes with a controller so it can be used like a conventional drone. The cost for installation on the Bao-8 will be 16K RMB ( approx 2,200 USD) I was really impressed with what I saw. For a little over 420K RMB (58K USD), you can have a top of the range Bao-8 with Huawei's latest ADS, and the LingYuan drone system. What a deal!

🚨🇨🇳🇨🇳Here are two of China's Top tech companies, BYD, the worlds leading NEV manufacturer, and DJI, the worlds leading drone maker collaborating to push technology to a new level. Last night I went to the press launch of BYD's Lingyuan Drone system, which is a drone pod on top of your car. It will initially be available on Boa-5, Bao-8, Yangwang U8 & N9, Tang L DM, Sea Lion o7 DM-i. It can return to the cabin from 2Km away, and can follow the vehicle at uptown 54Km/h, with three centimetre obstacle avoidance accuracy. It can be launched with one press of a button and can follow the vehicle without any additional user control. The drone is a slightly modified version of the DJI Mavic Air 3S which has lidar obstacle detection, and also comes with a controller so it can be used like a conventional drone. The cost for installation on the Bao-8 will be 16K RMB ( approx 2,200 USD) I was really impressed with what I saw. For a little over 420K RMB (58K USD), you can have a top of the range Bao-8 with Huawei's latest ADS, and the LingYuan drone system. What a deal!

Barrett

111,328 просмотров • 1 год назад

I recreated Meta’s new AI Glasses on my Quest, and the complete source code is available! 👓 Hey everyone! I’ve received a lot of feedback and messages about my latest project, where I used the Quest to recreate the features shown during Meta’s keynote. To answer your questions I decided to create a cool video to explain how I built it but also, share the full source code on Patreon 👇 Watch the making-of: Download the source code: This project includes: ✨ A UI navigable with microgestures 🤏 A pinch & twist hand-tracking input system 🎧 An audio player with volume control via pinch & twist 📸 A camera that zooms with pinch & twist 🖼️ A carousel to browse captured pictures 🧠 And a contextual AI that reacts to what you’re looking at Hope you guys will like it and that it will help you on your own project!! 😊

I recreated Meta’s new AI Glasses on my Quest, and the complete source code is available! 👓 Hey everyone! I’ve received a lot of feedback and messages about my latest project, where I used the Quest to recreate the features shown during Meta’s keynote. To answer your questions I decided to create a cool video to explain how I built it but also, share the full source code on Patreon 👇 Watch the making-of: Download the source code: This project includes: ✨ A UI navigable with microgestures 🤏 A pinch & twist hand-tracking input system 🎧 An audio player with volume control via pinch & twist 📸 A camera that zooms with pinch & twist 🖼️ A carousel to browse captured pictures 🧠 And a contextual AI that reacts to what you’re looking at Hope you guys will like it and that it will help you on your own project!! 😊

Quentin Valembois

15,977 просмотров • 9 месяцев назад

HE BUILT A BRAND SITE THAT LOOKS LIKE IT COST $10,000 - IN ONE EVENING WITH CLAUDE CODE What's on screen isn't a template with a color swap It's a full product brand site with effects most designers reach for Figma plugins to get What's actually on the page: > A headline that glows like it's lit from the inside > The product breaking out of the frame and sitting in front of the text > Typography that stops you mid-scroll > Depth that usually takes a motion designer to pull off > Details so considered it looks like someone art directed every pixel What it normally takes: > A brand designer who knows typography at this level > A frontend developer who knows when to reach for z-index and when not to > Back and forth between them until the details land right The price gap: > Brand site at this quality: $5,000 - $8,000+ > Your cost: a Claude subscription > Timeline: an evening Full walkthrough in the article below

monokern

518,288 просмотров • 16 дней назад

⚡️🇺🇦🇷🇺There is now confirmation of the first attempts to blind Russian FPV drones by installing small IR spotlights on Ukrainian drones, producing a bright, directional beam aimed at the incoming drone’s camera. In one case, a Ukrainian UGV tried to blind an approaching Russian FPV drone but failed. The second example was more successful a “Baba Yaga” drone was able to confuse the operator of an anti-drone interceptor that was attempting to shoot it down. For now, this cannot be called widespread, but in the future such a blinding system may become an effective method of countering FPV drones, especially fiber-optic-guided ones, which cannot be jammed by electronic warfare at all. And, as can be seen, the system can be installed on both ground and aerial platforms. So it turns out the outdated Passive Protection Systems (PPS) like the Shtora-1 so common in the late Soviet/Russian/Ukranian like the T-90A are state of the art now.

⚡️🇺🇦🇷🇺There is now confirmation of the first attempts to blind Russian FPV drones by installing small IR spotlights on Ukrainian drones, producing a bright, directional beam aimed at the incoming drone’s camera. In one case, a Ukrainian UGV tried to blind an approaching Russian FPV drone but failed. The second example was more successful a “Baba Yaga” drone was able to confuse the operator of an anti-drone interceptor that was attempting to shoot it down. For now, this cannot be called widespread, but in the future such a blinding system may become an effective method of countering FPV drones, especially fiber-optic-guided ones, which cannot be jammed by electronic warfare at all. And, as can be seen, the system can be installed on both ground and aerial platforms. So it turns out the outdated Passive Protection Systems (PPS) like the Shtora-1 so common in the late Soviet/Russian/Ukranian like the T-90A are state of the art now.

SIMPLICIUS Ѱ

46,911 просмотров • 7 месяцев назад

Wow. Recreating the Shawshank Redemption prison in 3D from a single video, in real time (!) Just read the MASt3R-SLAM paper and it's pretty neat. These folks basically built a real-time dense SLAM system on top of MASt3R, which is a transformer-based neural network that can do 3d reconstruction and localization from uncalibrated image pairs. The cool part is they don't need a fixed camera model -- it just works with arbitrary cameras -- think different focal lengths, sensor sizes, even handling zooming in video (FMV drone video anyone?!). If you've done photogrammetry or played with NeRFs you know that is a HUGE deal. They've solved some tricky problems like efficient point matching and tracking, plus they've figured out how to fuse point clouds and handle loop closures in real-time. Their system runs at about 15 FPS on a 4090 and produces both camera poses and dense geometry. When they know the camera calibration, they get SOTA results across several benchmarks, but even without calibration, they still perform well. What's interesting is the approach -- most recent SLAM work has built on DROID-SLAM's architecture, but these folks went a different direction by leveraging a strong 3D reconstruction prior. Seems to give them more coherent geometry, which makes sense since that's what MASt3R was designed for. For anyone who cares about monocular SLAM and 3D reconstruction, this feels like a significant step toward plug-and-play dense SLAM without calibration headaches -- perfect for drones, robots, AR/VR -- the works!

Wow. Recreating the Shawshank Redemption prison in 3D from a single video, in real time (!) Just read the MASt3R-SLAM paper and it's pretty neat. These folks basically built a real-time dense SLAM system on top of MASt3R, which is a transformer-based neural network that can do 3d reconstruction and localization from uncalibrated image pairs. The cool part is they don't need a fixed camera model -- it just works with arbitrary cameras -- think different focal lengths, sensor sizes, even handling zooming in video (FMV drone video anyone?!). If you've done photogrammetry or played with NeRFs you know that is a HUGE deal. They've solved some tricky problems like efficient point matching and tracking, plus they've figured out how to fuse point clouds and handle loop closures in real-time. Their system runs at about 15 FPS on a 4090 and produces both camera poses and dense geometry. When they know the camera calibration, they get SOTA results across several benchmarks, but even without calibration, they still perform well. What's interesting is the approach -- most recent SLAM work has built on DROID-SLAM's architecture, but these folks went a different direction by leveraging a strong 3D reconstruction prior. Seems to give them more coherent geometry, which makes sense since that's what MASt3R was designed for. For anyone who cares about monocular SLAM and 3D reconstruction, this feels like a significant step toward plug-and-play dense SLAM without calibration headaches -- perfect for drones, robots, AR/VR -- the works!

Bilawal Sidhu

703,816 просмотров • 1 год назад