Robotics
Showing 36 of 420 projects
A ROS library for LiDAR point cloud segmentation, enabling ground removal and object clustering for autonomous vehicle perception.
ROS 2 tool for recording and playback of timestamped messages from robotic systems.
A C++ framework for simulating and measuring ROS 2 system performance, tracking latency, reliability, CPU, and memory usage.
A generic framework for task planning and dispatch in ROS systems, encapsulating planning with common ROS library interfaces.
A graphical application for rapidly prototyping and deploying computer vision algorithms, primarily for robotics.
A ROS node that streams robot camera feeds and image topics over HTTP in multiple video formats for web browser viewing.
A CANopen driver framework for ROS enabling communication with industrial CANopen devices.
A metapackage containing standard ROS 2 message and service interface definitions for inter-stack communication.
A Python library for representing and manipulating quaternions, designed for 3D animation and geometry.
A pure Go library for building ROS 1 client nodes, enabling lightweight cross-platform robotics and data streaming applications.
A web-based server that catalogs ROS bag files, provides search and download capabilities, and offers a REST API for automation.
A ROS-based motion-primitives planner for fast and agile autonomous exploration with aerial robots.
An open-source Python toolbox for robot kinematics, dynamics, trajectory generation, and calibration.
A collection of ROS packages for advanced visualization tools and plugins for RViz and RQt.
A C++/TensorRT inference module for RangeNet++, enabling fast LiDAR semantic segmentation for robotics applications.
A hardware-accelerated Python library for running Quality-Diversity and neuroevolution algorithms in minutes instead of days.
A ROS2-based package for Simultaneous Localization and Mapping (SLAM) using AprilTag fiducial markers.
Open source software and hardware for vehicle control, stabilization, autonomous vehicles, and robotics.
A real-time ROS 2 package for detecting drivable roads and sidewalks from LIDAR point clouds in urban autonomous driving scenarios.
A C++11 library for efficient robotic localization and mapping, designed for low-resource hardware like Raspberry Pi.
A simple, multi-language implementation of the Iterative Closest Point algorithm for 3D point cloud registration.
A MicroPython library implementing the Madgwick algorithm for sensor fusion, calculating heading, pitch, and roll from motion tracking devices.
A full LiDAR SLAM system for static environment mapping using LiDAR with optional GPS, IMU, and odometry support.
A ROS 2 node for detecting AprilTag fiducial markers in camera images and publishing their poses and metadata.
A ROS 2 control driver for ODrive motor controllers, enabling integration of ODrive hardware into ROS 2 robotic systems.
A ROS package for visualizing motion plans, support areas, contact forces, and trajectories for legged robots in RVIZ.
Real-time Bayesian terrain traversability mapping and motion planning system for ROS-compatible unmanned ground vehicles using LiDAR point clouds.
A robust C++ sensor fusion library for online localization using factor graphs and least squares optimization.
A Python 3 library for controlling stepper motors, DC motors, and servos with a Raspberry Pi.
A JAX library implementing Lie groups for rigid body transformations in computer vision and robotics.
An event-driven, asynchronous, behavioral state machine library for real-time ROS applications written in C++.
Portable reference implementation of the Cyphal protocol stack in C++ for embedded systems and Linux.
A Python library for converting ROS messages to and from NumPy arrays, enabling efficient data manipulation in robotics.
A Python library for loading, manipulating, saving, and visualizing URDF (Unified Robot Description Format) files.
RVizWeb brings the ROS visualization tool RViz to the browser, enabling web-based 3D robot data visualization.
ROS 2 wrapper for Stereolabs ZED cameras, providing access to depth, point clouds, object detection, and spatial mapping.
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