Showing 2 open source projects for "grids"

View related business solutions
  • $300 Free Credits to Build on Google Cloud Icon
    $300 Free Credits to Build on Google Cloud

    New customers can spin up VMs, build with AI, and query data at no cost.

    Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
    Start Free
  • Host LLMs in Production With On-Demand GPUs Icon
    Host LLMs in Production With On-Demand GPUs

    NVIDIA L4 GPUs. 5-second cold starts. Scale to zero when idle.

    Deploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
    Start Free
  • 1
    Filt8- v3.6 alerts for WSJT-X FT8

    Filt8- v3.6 alerts for WSJT-X FT8

    Alert and filter QSO for WSJT-X FT8 FT4

    ...Offline Maps to show station grid locations as you decode them in real-time (Check out js8mapper project on https://sourceforge.net/projects/js8map/ for this same option for JS8Call) Map feature allows filtering by band, listings by callsigns or grids, and callsign lookups via QRZ or HamCall (this requires Internet). Filters & alerts WSJT-X FT8 and FT4 output for Mac/Linux/Win. Like JTAlert but minimalist and cross-platform. Works on low end systems such as Raspberry PI or laptop with minimal config, no internet required. Written in Python Problems? Email us at snitch.lyric0z@icloud.com
    Downloads: 1 This Week
    Last Update:
    See Project
  • 2
    LaMa

    LaMa

    LaMa - A Localization and Mapping library

    LaMa is a C++11 software library for robotic localization and mapping developed at the Intelligent Robotics and Systems (IRIS) Laboratory at the University of Aveiro - Portugal. It includes a framework for 3D volumetric grids (for mapping), a localization algorithm based on scan matching, and two SLAM solutions (an Online SLAM and a Particle Filter SLAM). The main feature is efficiency. Low computational effort and low memory usage whenever possible. The minimum viable computer to run our localization and SLAM solutions is a Raspberry Pi 3 Model B+. We provide a fast scan-matching approach to mobile robot localization supported by a continuous likelihood field. ...
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next