Apollo is a high performance, flexible architecture which accelerates the development, testing, and deployment of Autonomous Vehicles. For business and partnership, please visit our website.
apollo autonomous-vehicles autonomous-driving autonomy self-driving-carPython codes for robotics algorithm. This is a Python code collection of robotics algorithms, especially for autonomous navigation.
robotics algorithm path-planning control animation localization slam cvxpy ekf autonomous-vehicles autonomous-driving mapping autonomous-navigationCARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. If you want to benchmark your model in the same conditions as in our CoRL’17 paper, check out Benchmarking.
simulator autonomous-vehicles autonomous-driving research ai artificial-intelligence computer-vision deep-learning deep-reinforcement-learning imitation-learning self-driving-car ue4 unreal-engine-4 cross-platformWebots is an open-source robot simulator released under the terms of the Apache 2.0 license. It provides a complete development environment to model, program and simulate robots, vehicles and biomechanical systems. You can download pre-compiled binaries for Windows, macOS and Linux of the latest release, as well as older releases and nightly builds.
open-source multi-platform simulator robot ai computer-vision robotics simulation physics-engine ros robots autonomous-vehicles fluid-dynamics 3d-engine robot-simulator webots robotics-simulation simulated-robotsAutoware is the world's first "all-in-one" open-source software for self-driving vehicles. The capabilities of Autoware are primarily well-suited for urban cities, but highways, freeways, mesomountaineous regions, and geofenced areas can be also covered. The code base of Autoware is protected by the Apache 2 License. Please use it at your own discretion. For safe use, we provide a ROSBAG-based simulation environment for those who do not own real autonomous vehicles. If you plan to use Autoware with real autonomous vehicles, please formulate safety measures and assessment of risk before field testing. You may refer to Autoware Wiki for Users Guide and Developers Guide.
planner detection ros calibration autonomous-vehicles 3d-map autoware autoware-developers tier-ivAirSim is a simulator for drones (and soon other vehicles) built on Unreal Engine. It is open-source, cross platform and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped in to any Unreal environment you want.
drones ai self-driving-car autonomous-vehicles autonomous-quadcoptor research computer-vision artificial-intelligence deeplearning deep-reinforcement-learning control-systems pixhawk cross-platform platform-independent airsim unreal-engine simulatorAPM Planner Ground Control Station (Qt)
gcs ground-control-station ardupilot arducopter arduplane autopilot pixhawk apm-planner ardurover copter plane autonomous-vehicles ros open-source robot-operating-systemSqueezeSeg is released under the BSD license (See LICENSE for details). The dataset used for training, evaluation, and demostration of SqueezeSeg is modified from KITTI raw dataset. For your convenience, we provide links to download the converted dataset, which is distrubited under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. The instructions are tested on Ubuntu 16.04 with python 2.7 and tensorflow 1.0 with GPU support.
deep-neural-networks autonomous-vehicles lidar-point-cloudCARMASM advances research and development to accelerate market readiness and deployment of cooperative driving automation, while advancing automated driving technology safety, security, data, and artificial intelligence. It encourages collaboration and participation by a community of engineers and researchers to advance understanding of cooperative driving automation using open source software (OSS) and agile project management practices. CARMA is a reusable, extensible platform for controlling SAE level 2 connected, automated vehicles (AVs). It provides a rich, generic API for third party plugins that implement vehicle guidance algorithms to plan vehicle trajectories. It is written in C++ and runs in a Robot Operating System (ROS) environment on Ubuntu. The platform can be reused on a variety of properly equipped vehicles. Migration has begun from the ROS 1 framework to ROS 2.
open-source cpp ros autonomous-driving autonomous-vehicles self-driving cooperative-driving-automation automated-vehiclesBurro is a platform for small-scale self-driving cars. Using Burro you can build either a RC-style Ackermann steering car, or a Differential steering car (like a three-wheel robot). Depending on your hardware Burro will automatically select and setup the right kind of vehicle each time you run it. Thus you may share the same SD card among different vehicles without any changes.
self-driving-car autonomous-vehicles autonomous-car self-driving rc-car raspberry-pi neural-networkThis project is developed and being maintained by the Microsoft Deep Learning and Robotics Garage Chapter. This is currently a work in progress. We will continue to add more tutorials and scenarios based on requests from our users and the availability of our collaborators.Autonomous Driving has transcended far beyond being a crazy moonshot idea over the last half decade or so. It is quickly becoming the biggest technology today that promises to shape our tomorrow, not very unlike when cars came into existence in the first place. Almost every single car manufacturer, every big technology company, and a number of very promising startups have been working on different aspects of autonomous driving to help shape this revolution. Some of the biggest drivers powering this change have been the recent advances in software (robotics and deep learning techniques), hardware technology (GPUs, FPGAs etc.) and cloud computing. Cloud platforms like Azure have enabled ingest and processing of large amounts of data, making it possible for companies to push for levels 4 and 5 of AD autonomy.
deep-learning self-driving-car autonomous-vehicles tensorflow cntk keras airsim autonomous-driving autonomous-driving-cookbookA homework solution for the Autonomous Mobile Systems class at Beuth Hochschule für Technik, Berlin with Prof. Dr. Volker Sommer. Runs a simulation of a VolksBot with (error-free) laser rangers. in the project directory to bootstrap a virtual machine that is preconfigured with Player/Stage. The VM will be downloaded if it doesn't already exist your local machine (which is likely if you run the command the first time).
player-controls player stage robotics opencv vagrant slam frontiers simulation bht-berlin control-systems autonomous-vehiclesA path planning algorithm based on Hybrid A* for trailer truck. I want to achieve this on autonomous vehicle (click the image to see movie).
autonomous-vehicles autonomous-driving autonomous-car julia julia-languageMATLAB sample codes for mobile robot navigation. Sample codes for localization.
matlab ekf-slam nelder-mead icp dijkstra robotics robot autonomous-driving autonomous-vehiclesOpen Basemap is a collaborative initiative towards enabling worldwide autonomous vehicle development. More information can be found here. Besides git you will also need to setup git-lfs on your system by following the instructions here.
autonomous-vehicles self-driving-cars perception path-planning localization semantic-maps 3d-map lidar robotics mapsThis library intends to provide a C++ implementation of the Responsibility Sensitive Safety model (RSS) for Autonomous Vehicles. RSS is described in the following papers. Potential users of this C++ library are encouraged to read these papers in order to become familiar with the concepts and functions provided by the library.
autonomous-vehicles safety rssMetacar is a 2D reinforcement learning environment for autonomous vehicles running in the browser. The project aims to let reinforcement learning be more accessible to everyone through solving fun problems. Metacar comes with a set of a predefined levels, some harder to address than others. More levels and possibile scenarios will be added soon (pedestrian, bikes...). Furthermore, the library let you create your own levels and personalize the environment to create your desired scenario. You can also take a look at the online demo.
reinforcement-learning self-driving-car autonomous-vehicles pixijs tensorflowjs browserEKF to fuse GPS, IMU and encoder readings to estimate the pose of a ground robot in the navigation frame. Wikipedia writes: In the extended Kalman filter, the state transition and observation models need not be linear functions of the state but may instead be differentiable functions.
gps-ins ekf-localization autonomous-vehicles kalman-filter state-estimation autonomous-agentsThis is my submission to the path planning project for term 3 of Udacity's self-driving car program. The goal is to create a path planning pipeline that would smartly, safely, and comfortably navigate a virtual car around a virtual highway with other traffic. We are given a map of the highway, as well as sensor fusion and localization data about our car and nearby cars. We are supposed to give back a set of map points (x, y) that a perfect controller will execute every 0.02 seconds. Navigating safely and comfortably means we don't bump into other cars, we don't exceed the maximum speed, acceleration and jerk requirements. Navigating smartly means we change lanes when we have to. Clone this repo.
udacity motion-planning path-planning self-driving-car autonomous-driving autonomous-vehiclesThis is the GitHub repo for Electron, an indoor delivery robot. The goal of the robot is to deliver items to cubicles in an office building, be it office supplies or food. The robot must be able to autonomously drive around a building while successfully retrieving and delivering these items constantly upon user request. We do this using ROS, deep learning, and various sensors. In order to move around a building autonomously, our robot creates a map of the building using SLAM (gmapping) with an RPLidar A1 and odometry. It later utilizes the same map to navigate throughout the building by localizing itself using AMCL (adaptive monte carlo localization). We do most of this using ROS (Robot Operating System) and RVIZ for visualization.
drones autonomous-vehicles jetson-tx2 delivery-bot
We have large collection of open source products. Follow the tags from
Tag Cloud >>
Open source products are scattered around the web. Please provide information
about the open source projects you own / you use.
Add Projects.