Popular GitHub Repositories for Micro Ros

Autoware - the world's leading open-source software project for autonomous driving

Machine Learning Containers for NVIDIA Jetson and JetPack-L4T

YOLO ROS: Real-Time Object Detection for ROS

Modular AI runtime for robots

Robot-centric elevation mapping for rough terrain navigation

[MICRO'23, MLSys'22] TorchSparse: Efficient Training and Inference Framework for Sparse Convolution on GPUs.

ROSA ? is an AI Agent designed to interact with ROS1- and ROS2-based robotics systems using natural language queries. ROSA helps robot developers inspect, diagnose, understand, and operate robots.

Visual SLAM/odometry package based on NVIDIA-accelerated cuVSLAM

Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

[IEEE RA-L'25] NavRL: Learning Safe Flight in Dynamic Environments (NVIDIA Isaac/Python/ROS1/ROS2)

Fast computer vision library for SFM, calibration, fiducials, tracking, image processing, and more.

Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds (The PyTorch implementation)

LeGO-LOAM, LIO-SAM, LVI-SAM, FAST-LIO2, Faster-LIO, VoxelMap, R3LIVE, Point-LIO, KISS-ICP, DLO, DLIO, Ada-LIO, PV-LIO, SLAMesh, ImMesh, FAST-LIO-MULTI, M-LOAM, LOCUS, SLICT, MA-LIO, CT-ICP, GenZ-ICP, iG-LIO, SR-LIO application and comparison on Gazebo and real-world datasets. Installation and config files are provided.

Gibson Environments: Real-World Perception for Embodied Agents

Ultralytics YOLOv8, YOLOv9, YOLOv10, YOLOv11, YOLOv12 for ROS 2

ROS MCP服务器旨在通过提供一系列功能来简化机器人运动的控制，这些功能允许精确操纵线性和角速度。

C++ implementation to Detect, track and classify multiple objects using LIDAR scans or point cloud

The Standard ROS JavaScript Library

Detect 6-DOF grasp poses in point clouds

ROS-LLM is a framework designed for embodied intelligence applications in ROS. It allows natural language interactions and leverages Large Language Models (LLMs) for decision-making and robot control. With an easy configuration process, this framework allows for swift integration, enabling your robot to operate with it in as little as ten minutes.