👋 Johannes Hötter

My goal is to help teams successfully transform their daily work with AI, for which I believe you need both software but also human-centered methods around education and e.g. trusted partners. Providing this is what we aim for at Kern.

Co-Founder & CEO

At Kern AI (previously onetask), we aim to build a dev environment for AI training data with which we aim to provide both higher-quality and higher-quantity training data. If you are interested in how we do this, check out our website www.kern.ai

Johannes Hötter

Julius Haas

NVlabs at NVIDIA has released 𝗣𝘆𝗖𝘂𝗩𝗦𝗟𝗔𝗠 on GitHub, the official Python bindings for their CUDA-accelerated cuVSLAM library. This brings real-time visual odometry and SLAM into Python workflows. GPU acceleration included. 

PyCuVSLAM supports a wide range of camera modes (including monocular, stereo, RGB-D, multicamera, and visual-inertial tracking) and can produce camera poses, map points, and loop closure information directly from live data or recorded datasets.

More specifically:

• It provides a comprehensive Python API for NVIDIA’s cuVSLAM SLAM and odometry capabilities. 
• Works with CUDA 12.6 for real-time performance on desktop GPUs and NVIDIA Jetson platforms (Ubuntu 22.04/24.04, JetPack 6.1/6.2).  
• Installation options include native, venv, Conda, and Docker workflows.  
• Comes with example scripts for monocular/stereo SLAM, visual-inertial pipelines, dataset integrations (EuRoC, KITTI, etc.), and more.  
• There’s support for ROS2 through the Isaac ROS cuVSLAM integration.

If you’re building SLAM pipelines for drones, mobile robots, or perception systems and prefer Python tooling, this is for you.

GitHub: https://lnkd.in/gkxWAvsk

Building AI Solutions | DMs open | M.Sc. Automation Engineering

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