Cool! New player in the room.

We need re-consider our stereo model! I will report the difference soon in our project.

Excited to share a video about the workflow of our project! From zero to navigation with TinyNav 🗺️➡️🤖 1️⃣ Build a map 2️⃣ Add POIs in the editor 3️⃣ Let your robots navigate, correctly react to obstacles, and find feasible paths Keep an eye on TinyNav — a ~3,000-line open-source

saw completely new design of the head. Unitree is going to navigate the robots using stereo camera, just like our open source project tinynav github.com/UniflexAI/tiny…

🚀 Introducing the TinyNav Bounty Program We’re rewarding the community for contributions that improve navigation, perception, and tooling in physical AI. 🧠 Bug reports 🧩 Code contributions 📝 Docs & tutorials 🎥 Demos & content Earn rewards while shaping the future of open

Working on SLAM for years, I’m constantly asked: which method is faster? So I made a one-line command to benchmark solver speed with the standard dubrovnik/problem-16-22106-pre.txt input: `docker run --rm uniflexai/slambench:latest` Run it, share your result, and tell me if

I’ve updated github.com/dvorak0/slambe… with more benchmark results, including row-major vs column-major comparisons and GTSAM SmartFactor benchmarks. Added a summary screenshot below — check it out!

Thanks for open-sourcing such great models. In our early test, Fast Foundation Stereo reached real-time performance (>13.5 FPS) with max disparity 64 on Jetson Orin NX — honestly much better than we expected. We’re planning to integrate it into our open-source navigation stack

Just updated it to include frontend benchmarks. The first version compares Harris + Optical Flow with ORB + Hamming matching. Keep watching: github.com/dvorak0/slambe… You can also easily reproduce the test with: docker run --rm --cpuset-cpus="0" uniflexai/slambench:latest

If you know your hardware well enough, you can still write code that's faster than today's AI. I wrote a Harris corner detector kernel that's 2× faster than OpenCV.

We expected FastFoundationStereo to be close to FoundationStereo. We were wrong. On Jetson Orin Nano: - FoundationStereo surprised us — lidar-quality point cloud - FastFoundationStereo is far worse (yes, it is indeed fast) - Retinify matches FastFoundationStereo's quality but is

Tuned a faster Harris frontend kernel on an A55 ARM CPU @ 1.5GHz. For 752×480 EuRoC frames: • 59.49 ms → 21.51 ms • 2.77x faster Harris Same output: • 159 detected points • 139 tracked points Pipeline runtime: • 68.43 ms → 43.20 ms

FoundationStereo >> FastFoundationStereo > Retinify

Excited to share that TinyNav now has an app! We’re making robot navigation more accessible for everyone. More updates are on the way—stay tuned.