Autonomous Vehicles
Showing 19 of 19 projects
A Python code collection and textbook for robotics algorithms, featuring implementations for localization, mapping, SLAM, path planning, and control.
A comprehensive collection of Python sample codes and a textbook for robotics algorithms, covering localization, mapping, SLAM, path planning, and control.
An open-source, high-performance platform for developing, testing, and deploying autonomous vehicles.
Open-source simulator for drones and autonomous vehicles built on Unreal Engine and Unity, designed for AI research.
Open-source autopilot software for controlling aircraft, multi-rotors, rovers, boats, and submarines.
An open-source simulator built on Unreal Engine for developing, training, and validating autonomous driving systems.
An open-source project for developing autonomous vehicle software with datasets, models, and ROS components.
A computationally efficient and robust LiDAR-inertial odometry (LIO) package using a tightly-coupled iterated Kalman filter.
An optimization-based multi-sensor state estimator for accurate self-localization in drones, cars, and AR/VR applications.
An open-source development environment for modeling, programming, and simulating robots, vehicles, and mechanical systems.
A Unity-based simulator for training self-driving car models using deep learning.
A lightweight, ground-optimized lidar odometry and mapping system for ROS-compatible unmanned ground vehicles.
A ROS/ROS2 multi-robot simulator for autonomous vehicles, built on Unity HDRP for high-fidelity testing.
A ROS package for real-time 6DOF SLAM using 3D LIDAR, featuring graph-based optimization with multiple sensor constraints.
A curated collection of datasets for Simultaneous Localization and Mapping (SLAM) research, categorized by topic, platform, and environment.
A Python library for creating UAV applications by communicating with drones via MAVLink.
A real-time Hybrid A* path planner for nonholonomic autonomous vehicles, generating smooth, efficient paths in unstructured environments.
A curated checklist of state-of-the-art research materials (datasets, papers, code) for interaction-aware trajectory prediction.
A robust, real-time LiDAR odometry and mapping package optimized for Livox LiDARs with small fields of view.
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