Showing 5 open source projects for "kdiff3-64bit-setup_0.9.98-2.exe"

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    lidarslam_ros2

    lidarslam_ros2

    ROS 2 package of 3D lidar slam using ndt/gicp registration

    ROS 2 package of 3D lidar slam using ndt/gicp registration and pose-optimization. ros2 slam package of the frontend using OpenMP-boosted gicp/ndt scan matching and the backend using graph-based slam.
    Downloads: 2 This Week
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  • 2
    Eagleye

    Eagleye

    Precise localization based on GNSS and IMU

    Eagleye is an open-source software for vehicle localization utilizing GNSS and IMU[1]. Eagleye provides highly accurate and stable vehicle position and orientation by using GNSS Doppler[2][3][4][5][6]. The flowchart of the algorithm is shown in the figure below. The algorithms in this software are based on the outcome of the research undertaken by the Machinery Information Systems Lab (Meguro Lab) at Meijo University.
    Downloads: 1 This Week
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  • 3
    Isaac ROS Visual SLAM

    Isaac ROS Visual SLAM

    Visual SLAM/odometry package based on NVIDIA-accelerated cuVSLAM

    Discover a faster, easier way to build advanced AI robotics applications with the NVIDIA Isaac™ ROS collection of accelerated computing packages and AI models, bringing NVIDIA acceleration to ROS developers everywhere. Isaac ROS Visual SLAM provides a high-performance, best-in-class ROS 2 package for VSLAM (visual simultaneous localization and mapping). This package uses one or more stereo cameras and optionally an IMU to estimate odometry as an input to navigation. It is GPU-accelerated to provide real-time, low-latency results in a robotics application. VSLAM provides an additional odometry source for mobile robots (ground-based) and can be the primary odometry source for drones. ...
    Downloads: 0 This Week
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  • 4
    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
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  • 5
    espeak-tswana is a branch of espeak project implementing Setswana (A Southern African Bantu speaking language) .
    Downloads: 0 This Week
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