PythonRobotics - Python sample codes for robotics algorithms.

  •        124

Python codes for robotics algorithm. This is a Python code collection of robotics algorithms, especially for autonomous navigation.

https://atsushisakai.github.io/PythonRobotics/
https://github.com/AtsushiSakai/PythonRobotics

Tags
Implementation
License
Platform

   




Related Projects

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.

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.

AirSim - Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research

  •    C++

AirSim 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.

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.

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.


simulator - A ROS/ROS2 Multi-robot Simulator for Autonomous Vehicles

  •    CSharp

Check out our latest news and subscribe to our mailing list to get the latest updates. LG Electronics America R&D Lab has developed an HDRP Unity-based multi-robot simulator for autonomous vehicle developers. We provide an out-of-the-box solution which can meet the needs of developers wishing to focus on testing their autonomous vehicle algorithms. It currently has integration with The Autoware Foundation's Autoware.auto and Baidu's Apollo platforms, can generate HD maps, and can be immediately used for testing and validation of a whole system with little need for custom integrations. We hope to build a collaborative community among robotics and autonomous vehicle developers by open sourcing our efforts.

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.

redtail - Perception and AI components for autonomous mobile robotics.

  •    C++

Autonomous visual navigation components for drones and ground vehicles using deep learning. Refer to wiki for more information on how to get started. This project contains deep neural networks, computer vision and control code, hardware instructions and other artifacts that allow users to build a drone or a ground vehicle which can autonomously navigate through highly unstructured environments like forest trails, sidewalks, etc. Our TrailNet DNN for visual navigation is running on NVIDIA's Jetson embedded platform. Our arXiv paper describes TrailNet and other runtime modules in detail.

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.

autoware.ai - Open-source software for self-driving vehicles

  •    

Autoware 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.

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.

LightNet - LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)

  •    Python

This repository contains the code (in PyTorch) for: "LightNet: Light-weight Networks for Semantic Image Segmentation " (underway) by Huijun Liu @ TU Braunschweig. Semantic Segmentation is a significant part of the modern autonomous driving system, as exact understanding the surrounding scene is very important for the navigation and driving decision of the self-driving car. Nowadays, deep fully convolutional networks (FCNs) have a very significant effect on semantic segmentation, but most of the relevant researchs have focused on improving segmentation accuracy rather than model computation efficiency. However, the autonomous driving system is often based on embedded devices, where computing and storage resources are relatively limited. In this paper we describe several light-weight networks based on MobileNetV2, ShuffleNet and Mixed-scale DenseNet for semantic image segmentation task, Additionally, we introduce GAN for data augmentation[17] (pix2pixHD) concurrent Spatial-Channel Sequeeze & Excitation (SCSE) and Receptive Field Block (RFB) to the proposed network. We measure our performance on Cityscapes pixel-level segmentation, and achieve up to 70.72% class mIoU and 88.27% cat. mIoU. We evaluate the trade-offs between mIoU, and number of operations measured by multiply-add (MAdd), as well as the number of parameters.

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.

Fast-Planner - A Robust and Efficient Trajectory Planner for Quadrotors

  •    C++

Fast-Planner is developed aiming to enable quadrotor fast flight in complex unknown environments. It contains a rich set of carefully designed planning algorithms. Mar 13, 2021: Code for fast autonomous exploration is available now! Check this repo for more details.

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.

Teach-Repeat-Replan - Teach-Repeat-Replan: A Complete and Robust System for Aggressive Flight in Complex Environments

  •    C++

If polyhedrons can't be visualized properly in Rviz, please delete the Display Type PolyhedronArray from the display menu, then manually add PolyhedronArray again and select the topic in its Topic drop-down list. If using Ubuntu 18.04 and ROS melodic, you may get "error: expected constructor, destructor, or type conversion before ‘(’ token PLUGINLIB_DECLARE_CLASS(router, RouterNode, RouterNode, nodelet::Nodelet);" during compiling. Follow issue#34 to fix it.

iris_lama - LaMa - A Localization and Mapping library

  •    C++

Developed and maintained by Eurico Pedrosa, University of Aveiro (C) 2019. LaMa is a C++11 software library for robotic localization and mapping developed at the Intelligent Robotics and Systems (IRIS) Laboratory from the University of Aveiro - Portugal. It includes a framework for 3D volumetric grids (for mapping), a localization algorithm based on scan matching and two SLAM solution (an Online SLAM and a Particle Filter SLAM).

VINS-Mobile - Monocular Visual-Inertial State Estimator on Mobile Phones

  •    C++

27 Jun 2017: We upgrade the pose outputs and AR rendering to 30 Hz by motion-only 3D tracking in front-end and improve the loop-closure procedure(See our technical report for detail). VINS-Mobile is a real-time monocular visual-inertial state estimator developed by members of the HKUST Aerial Robotics Group. It runs on compatible iOS devices, and provides localization services for augmented reality (AR) applications. It is also tested for state estimation and feedback control for autonomous drones. VINS-Mobile uses sliding window optimization-based formulation for providing high-accuracy visual-inertial odometry with automatic initialization and failure recovery. The accumulated odometry errors are corrected in real-time using global pose graph SLAM. An AR demonstration is provided to showcase its capability.

cartographer - Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations

  •    C++

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations. You can find information about contributing to Cartographer at our Contribution page.

AB3DMOT - (IROS 2020, ECCVW 2020) Official Python Implementation for "3D Multi-Object Tracking: A Baseline and New Evaluation Metrics"

  •    Python

3D multi-object tracking (MOT) is an essential component technology for many real-time applications such as autonomous driving or assistive robotics. However, recent works for 3D MOT tend to focus more on developing accurate systems giving less regard to computational cost and system complexity. In contrast, this work proposes a simple yet accurate real-time baseline 3D MOT system. We use an off-the-shelf 3D object detector to obtain oriented 3D bounding boxes from the LiDAR point cloud. Then, a combination of 3D Kalman filter and Hungarian algorithm is used for state estimation and data association. Although our baseline system is a straightforward combination of standard methods, we obtain the state-of-the-art results. To evaluate our baseline system, we propose a new 3D MOT extension to the official KITTI 2D MOT evaluation along with two new metrics. Our proposed baseline method for 3D MOT establishes new state-of-the-art performance on 3D MOT for KITTI, improving the 3D MOTA from 72.23 of prior art to 76.47. Surprisingly, by projecting our 3D tracking results to the 2D image plane and compare against published 2D MOT methods, our system places 2nd on the official KITTI leaderboard. Also, our proposed 3D MOT method runs at a rate of 214.7 FPS, 65 times faster than the state-of-the-art 2D MOT system. 1. Clone the github repository.






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.