Displaying 1 to 20 from 35 results

apollo - An open autonomous driving platform

  •    C++

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.

carla - Open-source simulator for autonomous driving research.

  •    C++

CARLA 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.

OpenPCDet - OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

  •    Python

OpenPCDet is a clear, simple, self-contained open source project for LiDAR-based 3D object detection. It is also the official code release of [PointRCNN], [Part-A^2 net] and [PV-RCNN].

waymo-open-dataset - Waymo Open Dataset

  •    C++

The Waymo Open Dataset was first launched in August 2019 with a perception dataset comprising high resolution sensor data and labels for 1,950 segments. We have released the Waymo Open Dataset publicly to aid the research community in making advancements in machine perception and autonomous driving technology. We expanded the Waymo Open Dataset to also include a motion dataset comprising object trajectories and corresponding 3D maps for over 100,000 segments. We have updated this repository to add support for this new dataset. Please refer to the Quick Start.

awesome-robotic-tooling - Tooling for professional robotic development in C++ and Python with a touch of ROS, autonomous driving and aerospace: https://freerobotics


To stop reinventing the wheel you need to know about the wheel. This list is an attempt to show the variety of open and free tools in software and hardware development, which are useful in professional robotic development. Your contribution is necessary to keep this list alive, increase the quality and to expand it. You can read more about it's origin and how you can participate in the contribution guide and related blog post. All new project entries will have a tweet from protontypes.

mrpt - :zap: The Mobile Robot Programming Toolkit (MRPT)

  •    C++

Mobile Robot Programming Toolkit (MRPT) provides C++ libraries aimed at researchers in mobile robotics and computer vision. Libraries include SLAM solutions, 3D(6D) geometry, SE(2)/SE(3) Lie groups, probability density functions (pdfs) over points, landmarks, poses and maps, Bayesian inference (Kalman filters, particle filters), image processing, obstacle avoidance, etc. MRPT also provides GUI apps for Stereo camera calibration, dataset inspection, and much more. See this PPA for nightly builds from the develop branch, or this one for stable releases.

highway-env - A minimalist environment for decision-making in autonomous driving

  •    Python

An episode of one of the environments available in highway-env. In this task, the ego-vehicle is driving on a multilane highway populated with other vehicles. The agent's objective is to reach a high speed while avoiding collisions with neighbouring vehicles. Driving on the right side of the road is also rewarded.

apollo-platform - Collections of Apollo Platform Software

  •    C++

The Apollo-platform will cover all the platform level support. In the first release, we add the most popular solution Robot Operating System (ROS) under ros directory. The Robot Operating System (ROS) is flexible framework for writing robot software. This release is originated from ROS Indigo release.

mscnn - Caffe implementation of our multi-scale object detection framework

  •    C++

This implementation is written by Zhaowei Cai at UC San Diego. MS-CNN is a unified multi-scale object detection framework based on deep convolutional networks, which includes an object proposal sub-network and an object detection sub-network. The unified network can be trained altogether end-to-end.

ecal - eCAL - enhanced Communication Abstraction Layer

  •    C++

The enhanced Communication Abstraction Layer (eCAL) is a middleware that enables scalable, high performance interprocess communication on a single computer node or between different nodes in a computer network. eCAL uses a publish - subscribe pattern to automatically connect different nodes in the network. Visit the eCAL Documentation at 🌐 http://ecal.io for more information.

dataset-api - The ApolloScape Open Dataset for Autonomous Driving and its Application.

  •    Jupyter

Apollo is a high performance, flexible architecture which accelerates the development, testing, and deployment of Autonomous Vehicles. ApolloScape, part of the Apollo project for autonomous driving, is a research-oriented dataset and toolkit to foster innovations in all aspects of autonomous driving, from perception, navigation, control, to simulation.

carma-platform - CARMA Platform is built on robot operating system (ROS) and utilizes open source software (OSS) that enables Cooperative Driving Automation (CDA) features to allow Automated Driving Systems to interact and cooperate with infrastructure and other vehicles through communication

  •    C++

CARMASM 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.

iscloam - Intensity Scan Context based full SLAM implementation for autonomous driving. ICRA 2020

  •    C++

This work is an implementation of paper "Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection" in IEEE International Conference on Robotics and Automation 2020 (ICRA) paper This work is 3D lidar based Simultaneous Localization And Mapping (SLAM), including both front-end and back-end SLAM, at 20Hz.

3d-bat - 3D Bounding Box Annotation Tool (3D-BAT) Point cloud and Image Labeling

  •    Javascript

1. Step: draw bounding box in the camera image 2. Step: choose current bounding box by activating it 3. Step: You can move it in image space or even change its size by drag and droping 4. Step: Switch into PCD MODE into birds-eye-view 5. Step: Place 3D label into 3D scene to corresponding 2D label 6. Step: Adjust label: 1. drag and dropping directly on label to change position or size 2. use control bar to change position and size (horizontal bar -> rough adjustment, vertical bar -> fine adjustment) 3. Go into camera view to check label with higher intensity and bigger point size 7. Step: Choose label from drop down list 8. Step: Repeat steps 1-7 for all objects in the scene 9. Step: Save labels into file 10. Step: Click on 'HOLD' button if you want to keep the same label positions and sizes 11. Step: click on 'Next camera image'

OpenCDA - A generalized framework for prototyping full-stack cooperative driving automation applications under CARLA+SUMO

  •    Python

OpenCDA is a SIMULATION tool integrated with a prototype cooperative driving automation (CDA; see SAE J3216) pipeline as well as regular automated driving components (e.g., perception, localization, planning, control). The tool integrates automated driving simulation (CARLA), traffic simulation (SUMO), and Co-simulation (CARLA + SUMO). OpenCDA is all in Python. The purpose is to enable researchers to fast-prototype, simulate, and test CDA algorithms and functions. By applying our simulation tool, users can conveniently conduct both task-specific evaluation (e.g. object detection accuracy) and pipeline-level assessment (e.g. traffic safety) on their customized algorithms.

AutonomousDrivingCookbook - Scenarios, tutorials and demos for Autonomous Driving

  •    Jupyter

This 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.

HybridAStarTrailer - A path planning algorithm based on Hybrid A* for trailer truck

  •    Julia

A path planning algorithm based on Hybrid A* for trailer truck. I want to achieve this on autonomous vehicle (click the image to see movie).

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