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Autonomous-Systems-Guide

Autonomous Systems Guide
https://github.com/mikeroyal/Autonomous-Systems-Guide

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Computer Vision Learning Resources
- Computer Vision
- Computer Vision
- OpenCV Courses
- Exploring Computer Vision in Microsoft Azure
- Top Computer Vision Courses Online | Coursera
- Top Computer Vision Courses Online | Udemy
- Learn Computer Vision with Online Courses and Lessons | edX
- Computer Vision and Image Processing Fundamentals | edX
- Introduction to Computer Vision Courses | Udacity
- Computer Vision Nanodegree program | Udacity
- Computer Vision Training Courses | NobleProg
- Visual Computing Graduate Program | Stanford Online
- Machine Vision Course |MIT Open Courseware
- Introduction to Computer Vision Courses | Udacity
- OpenCV Courses
- Computer Vision
Autodesk Tools and Frameworks
- Fabrication ESTmep™, CADmep™, and CAMduct™ - alone or in the Architecture, Engineering & Construction Collection..
- Formit - based 3D sketching. The pro version of FormIt includes the tools in the FormIt app, plus Dynamo computation, and collaboration and analysis features.
- Helius PFA
- HSMWorks - embedded 2.5 to 5-axis milling, turning, and mill-turn capabilities. HSMWorks is included with your Fusion 360 subscription.
- Within Medical®
- Vault PLM - wide collaboration and product lifecycle management.
- Fusion 360 Manage
- Tinkercad® - to-use app for 3D design, electronics, and coding. It's used by teachers, kids, hobbyists, and designers to imagine, design, and make anything.
- AEC(Architecture, Engineering & Construction) Collection® - based common data environment that facilitates project delivery from early-stage design through to construction.
- Fusion 360®
- Fusion 360 with FeatureCAM®
- Fusion 360 with Netfabb®
- Fusion Team - based coll tool that helps eliminate the inefficiencies that disparate tools create when working with your internal and external teams.
- Fusion 360 with PowerInspect®
- Fusion 360 with PowerShape®
- Autodesk PartMaker® - spindle machining operations. These can be used for turning, indexed and interpolated C-axis milling, Y-axis, and B-axis milling.
- Robot Structural Analysis Professional - integrated workflows to exchange data with Revit. It can help you to create more resilient, constructible designs that are accurate, coordinated, and connected to BIM.
- Revit LT™ - effective BIM (Building Information Modeling) solution, you can produce high-quality 3D architectural designs and documentation.
- Arnold
- Maya LT™ - looking characters, props, and environments using the sophisticated 3D modeling and animation tools.
- Flame®
- ReCap™ - built conditions to gain insights and make better decisions.
- Character Generator® - based laboratory to create fully rigged 3D characters for animation packages and game engines.
- Smoke® - based compositing tools in a timeline-centered editing environment.
- ShotGrid
- Advance Steel®
- Media & Entertainment Collection®
- Civil 3D®
- Inventor® CAM - embedded 2.5-axis to 5-axis milling, turning, and mill-turn capabilities.
- Product Design & Manufacturing Collection - grade applications that connect everyone, from concept to production, with shared tools to streamline your product development process.
- InfraWorks®
- SketchBook®
- Alias®
- Assemble BIM Data - in-place tracking, and more.
- Autodesk® Forge - based developer platform from Autodesk. That let's you access design and engineering data in the cloud with the Forge platform. Whether you want to automate processes, connect teams and workflows, or visualize your data using Forge APIs.
- Autodesk® CFD
- Autodesk® Drive
- Autodesk® Build
- Autodesk® Takeoff
- BuildingConnected - time construction network that connects owners and builders through an easy-to-use platform to streamline the bid and risk management process.
- Bid Board Pro
- TradeTapp
- Helius Composite - in solvers minimize the need to have secondary finite element analysis (FEA) software to analyze material characteristics more quickly.
- Insight - efficient buildings with advanced simulation engines and building performance analysis data integrated in Revit.
- Moldflow®
- MotionBuilder®
- PlanGrid Build
- Point Layout
- Structural Bridge Design® - span bridges used by engineers to deliver design reports faster.
- Vault®
- Vehicle Tracking®
- VRED® - rendering modes.
- Pype
- Pype Closeout
- Pype SmartPlans
- CAMplete - code post-processing, verification, and simulation for different kinds of CNC machinery. Import data from leading CAM software then use proven post-processors and highly accurate 3D machine models, developed in partnership with machine tool builders, to rapidly produce high-quality, collision free NC machining code.
- Assemble BIM Data - in-place tracking, and more.
- Autodesk® Takeoff
- BuildingConnected - time construction network that connects owners and builders through an easy-to-use platform to streamline the bid and risk management process.
- Autodesk® Rendering - resolution cloud rendering software that let's you produce stunning, high-quality renderings from designs and models with cloud rendering. This service uses cloud credits, which is a universal measure across Autodesk consumption-based cloud services to perform certain tasks in the cloud.
- Autodesk® Viewer
- TradeTapp
- EAGLE
- PlanGrid Build
- Revit®
- Autodesk® Forge - based developer platform from Autodesk. That let's you access design and engineering data in the cloud with the Forge platform. Whether you want to automate processes, connect teams and workflows, or visualize your data using Forge APIs.
- Autodesk BIM 360®
- Bid Board Pro
- Design Review
- Pype
- AutoCAD LT® - aided design (CAD) software that architects, engineers, construction professionals, and designers rely on to design, draft, and document with precise 2D geometry.
- AutoCAD® Mobile App
- AutoCAD® Web App
- Autodesk
- Inventor Nastran® - embedded finite element analysis software that delivers finite element analysis (FEA) tools for engineers and analysts. Simulation covers multiple analysis types, such as linear and nonlinear stress, dynamics, and heat transfer.
- Inventor® Nesting - embedded, true-shape nesting tools for Inventor that helps you optimize yield from flat raw material. Easily compare nesting studies to optimize efficiency and reduce costs, and export 3D models or DXF™ files of the completed nest for cutting path generation.
- Inventor Tolerance Analysis® - embedded tolerance stackup analysis software that is designed to help Inventor users make more informed decisions while specifying manufacturing tolerances.
- Autodesk® Takeoff
- Bid Board Pro
- TradeTapp
- BIM Collaborate Pro - based design collaboration and coordination software that connects AEC teams, helping you execute on design intent and deliver high-quality constructible models on a single platform.
- Autodesk
- Autodesk
- Autodesk
- Autodesk
- Autodesk
- Autodesk
Computer Vision Tools, Libraries, and Frameworks
- Fuzzy logic - tree processing and better integration with rules-based programming.
- ResearchGate
- Support Vector Machine (SVM) - group classification problems.
- OpenClipArt
- IBM
- Convolutional Neural Networks (R-CNN)
- CS231n
- Recurrent neural networks (RNNs)
- Slideteam
- Random forest - used machine learning algorithm, which combines the output of multiple decision trees to reach a single result. A decision tree in a forest cannot be pruned for sampling and therefore, prediction selection. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems.
- wikimedia
- Decision trees - structured models for classification and regression.
- CMU
- Naive Bayes - theorem.html) with strong independence assumptions between the features.
- mathisfun
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- Vision HDL Toolbox™ - streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
- wikimedia
- wikimedia
- wikimedia
- LRSLibrary - Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
- Deep Learning HDL Toolbox™ - built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
- wikimedia
- wikimedia
- wikimedia
- wikimedia
- Deep Learning Toolbox™ - term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
- Mapping Toolbox™
- ROS Toolbox
- Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
- Data Acquisition Toolbox™
- Statistics and Machine Learning Toolbox™
- Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
- Support Vector Machine (SVM) - group classification problems.
- Parallel Computing Toolbox™ - intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
- Partial Differential Equation Toolbox™
- Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
- Microsoft AirSim - source, cross platform, and supports [software-in-the-loop simulation](https://www.mathworks.com/help///ecoder/software-in-the-loop-sil-simulation.html) with popular flight controllers such as PX4 & ArduPilot and [hardware-in-loop](https://www.ni.com/en-us/innovations/white-papers/17/what-is-hardware-in-the-loop-.html) with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
- Computer Vision Toolbox™
- Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
- Automated Driving Toolbox™ - eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
- Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
- DeepAI
- UAV Toolbox
CUDA Tools Libraries, and Frameworks
- CuPy - compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. It supports a subset of numpy.ndarray interface.
- cuDF - like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.
- ArrayFire - purpose library that simplifies the process of developing software that targets parallel and massively-parallel architectures including CPUs, GPUs, and other hardware acceleration devices.
- AresDB - powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management.
- GraphVite - speed and large-scale embedding learning in various applications.
- CUDA Toolkit - accelerated applications. The CUDA Toolkit allows you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to build and deploy your application on major architectures including x86, Arm and POWER.
- CUDA-X HPC - X HPC includes highly tuned kernels essential for high-performance computing (HPC).
- Minkowski Engine - differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unpooling, and broadcasting operations for sparse tensors.
- Thrust - level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs.
- cuML - learn.
- NVIDIA Container Toolkit - container) and utilities to automatically configure containers to leverage NVIDIA GPUs.
- CUTLASS - performance matrix-multiplication (GEMM) at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS.
- CUB
- Arraymancer - dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing ecosystem.
- Kintinuous - time dense visual SLAM system capable of producing high quality globally consistent point and mesh reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor.
- CatBoost
- NVIDIA cuDNN - accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
- Numba - aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
- Tensorman
- Chainer - based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.
MATLAB Learning Resources
- MATLAB
- MATLAB Documentation
- MATLAB and Simulink Training from MATLAB Academy
- MathWorks Certification Program
- MATLAB Online Courses from Udemy
- MATLAB Online Courses from Coursera
- MATLAB Online Courses from edX
- Building a MATLAB GUI
- MATLAB Style Guidelines 2.0
- Setting Up Git Source Control with MATLAB & Simulink
- Pull, Push and Fetch Files with Git with MATLAB & Simulink
- Create New Repository with MATLAB & Simulink
- PRMLT
- Getting Started with MATLAB
- MathWorks Certification Program
Learning Resources for ML
- Machine Learning
- Machine Learning by Stanford University from Coursera
- AWS Training and Certification for Machine Learning (ML) Courses
- Machine Learning Scholarship Program for Microsoft Azure from Udacity
- Microsoft Certified: Azure Data Scientist Associate
- Microsoft Certified: Azure AI Engineer Associate
- Azure Machine Learning training and deployment
- Learning Machine learning and artificial intelligence from Google Cloud Training
- JupyterLab
- Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances
- How to run Jupyter Notebooks in your Azure Machine Learning workspace
- Machine Learning Courses Online from Udemy
- Machine Learning Courses Online from Coursera
- Learn Machine Learning with Online Courses and Classes from edX
- Machine Learning Scholarship Program for Microsoft Azure from Udacity
- Machine Learning Crash Course for Google Cloud
ML Frameworks, Libraries, and Tools
- TensorFlow - to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
- Keras - level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
- PyTorch
- Amazon SageMaker
- Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
- Apple CoreML - tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data.
- Apache OpenNLP - source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).
- Apache Airflow - source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
- Open Neural Network Exchange(ONNX) - in operators and standard data types.
- Apache MXNet
- Anaconda
- Weka - in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
- Jupyter Notebook - source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
- Apache Spark - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
- Apache PredictionIO
- BigDL
- Eclipse Deeplearning4J (DL4J) - based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
- XGBoost
- OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
- PlaidML
- Caffe
- Theano - dimensional arrays efficiently including tight integration with NumPy.
- nGraph - of-use to AI developers.
- Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
- Cluster Manager for Apache Kafka(CMAK)
- Apache MXNet
- Scikit-Learn
CUDA Learning Resources
- NVIDIA Deep Learning cuDNN Documentation
- CUDA Toolkit Documentation
- CUDA Quick Start Guide
- CUDA on WSL
- CUDA GPU support for TensorFlow
- NVIDIA GPU Cloud Documentation
- NVIDIA NGC - optimized software for deep learning, machine learning, and high-performance computing (HPC) workloads.
- NVIDIA NGC Containers - accelerated software for AI, machine learning and HPC. These containers take full advantage of NVIDIA GPUs on-premises and in the cloud.
- NVIDIA NGC Containers - accelerated software for AI, machine learning and HPC. These containers take full advantage of NVIDIA GPUs on-premises and in the cloud.
- CUDA GPU support for TensorFlow
- CUDA - accelerated applications, the sequential part of the workload runs on the CPU, which is optimized for single-threaded. The compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers can program in popular languages such as C, C++, Fortran, Python and MATLAB.
MATLAB Tools, Libraries, Frameworks
- MATLAB and Simulink Services & Applications List
- MATLAB in the Cloud - cloud) including [AWS](https://aws.amazon.com/) and [Azure](https://azure.microsoft.com/).
- MATLAB Online™
- Simulink - Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.
- Simulink Online™
- MATLAB Drive™
- SoC Blockset™
- Wireless HDL Toolbox™ - verified, hardware-ready Simulink® blocks and subsystems for developing 5G, LTE, and custom OFDM-based wireless communication applications. It includes reference applications, IP blocks, and gateways between frame and sample-based processing.
- ThingSpeak™ - of-concept IoT systems that require analytics.
- hctsa - series analysis using Matlab.
- Plotly
- YALMIP
- GNU Octave - level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.
- MATLAB Schemer
- SEA-MAT
- Gramm - level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.
- Plotly
- GNU Octave - level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.
- Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
- hctsa - series analysis using Matlab.
Photogrammetry Tools, Libraries, and Frameworks
- Agisoft Metashape - alone software product that performs photogrammetric processing of digital images and generates 3D spatial data to be used in GIS applications, cultural heritage documentation, and visual effects production as well as for indirect measurements of objects of various scales.
- Autodesk® ReCap™
- Autodesk® ReCap™ Photo - connected solution tailored for drone/UAV photo capturing workflows. Using ReCap Photo, you can create textured meshes, point clouds with geolocation, and high-resolution orthographic views with elevation maps.
- Pix4D
- PIX4Dmapper
- RealityCapture - of-the-art photogrammetry software solution that creates virtual reality scenes, textured 3D meshes, orthographic projections, geo-referenced maps and much more from images and/or laser scans completely automatically.
- Adobe Scantastic - based photogrammetry pipeline), users can easily scan objects in their physical environment and turn them into 3D models which can then be imported into tools like [Adobe Dimension](https://www.adobe.com/products/dimension.html) and [Adobe Aero](https://www.adobe.com/products/aero.html).
- Adobe Aero - party apps like Cinema 4D, or asset libraries like Adobe Stock and TurboSquid. Aero optimizes a wide array of assets, including OBJ, GLB, and glTF files, for AR, so you can visualize them in real time.
- MicroStation
- Leica Photogrammetry Suite (LPS) - friendly environment that guarantees results even for photogrammetry novices.
- Terramodel
- COLMAP - purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for reconstruction of ordered and unordered image collections.
- Multi-View Environment (MVE) - view datasets and to support the development of algorithms based on multiple views. It features Structure from Motion, Multi-View Stereo and Surface Reconstruction. MVE is developed at the TU Darmstadt.
- PhotoModeler - effective way for accurate 2D or 3D measurement, photo-digitizing, surveying, 3D scanning, and reality capture.
- ODM
- WebODM - friendly, commercial grade software for drone image processing. Generate georeferenced maps, point clouds, elevation models and textured 3D models from aerial images. It supports multiple engines for processing, currently [ODM](https://github.com/OpenDroneMap/ODM) and [MicMac](https://github.com/dronemapper-io/NodeMICMAC/).
- NodeODM
- FIELDimageR
- Regard3D - from-motion program. It converts photos of an object, taken from different angles, into a 3D model of this object.
- MicroStation
- Leica Photogrammetry Suite (LPS) - friendly environment that guarantees results even for photogrammetry novices.
- MicMac - source photogrammetry software tools for 3D reconstruction.
- Meshroom - source 3D Reconstruction Software based on the AliceVision framework.
Photogrammetry Learning Resources
- Top Photogrammetry Courses Online | Udemy
- Photogrammetry With Drones: In Mapping Technology | Udemy
- Introduction to Photogrammetry Course | Coursera
- Photogrammetry Online Classes and Training | Linkedin Learning
- Pix4D training and certification for mapping professionals
- Drone mapping and photogrammetry workshops with Pix4D
- Digital Photogrammetric Systems Course | Purdue Online Learning
- Photogrammetry Training | Deep3D Photogrammetry
- ASPRS Certification Program
- Terrestrial(Close-range) photogrammetry
Autodesk Learning Resources
- Autodesk
- CNC programming (Computer Numerical Control Programming)
- AutoDesk Learning & Training
- Autodesk Certification
- Autodesk University
- Autodesk Design Academy
- Autodesk Customer Success Hub
- Software and Services for Education | Autodesk Education
- AutoDesk Forums
- Top Autodesk Courses on Coursera
- Top Autodesk Fusion 360 Courses on Coursera
- Learning Civil 3D on Autodesk Knowledge Network
- AutoDesk Developer Network
- Top Autodesk Courses on Udemy
- Autodesk Customer Success Hub
LiDAR Learning Resources
- Introduction to Lidar Course - NOAA
- Lidar 101:An Introduction to Lidar Technology, Data, and Applications(PDF) - NOAA
- Understanding LiDAR Technologies - GIS Lounge
- LiDAR University Free Lidar Training Courses on MODUS AI
- LiDAR | Learning Plan on ERSI
- Light Detection and Ranging Sensors Course on Coursera
- Quick Introduction to Lidar and Basic Lidar Tools(PDF)
- LIDAR - GIS Wiki
- OpenStreetMap Wiki
- OpenStreetMap Frameworks
- Back to the Top
LiDAR Tools & Frameworks
- Light Detection and Ranging (lidar) - resolution models of ground elevation with a vertical accuracy of 10 centimeters (4 inches). Lidar equipment, which includes a laser scanner, a Global Positioning System (GPS), and an Inertial Navigation System (INS), is typically mounted on a small aircraft. The laser scanner transmits brief pulses of light to the ground surface. Those pulses are reflected or scattered back and their travel time is used to calculate the distance between the laser scanner and the ground.  Lidar data is initially collected as a “point cloud” of individual points reflected from everything on the surface, including structures and vegetation. To produce a “bare earth” Digital Elevation Model (DEM), structures and vegetation are stripped away.
- Mola
- MOLA
- LASer(LAS) - dimensional point cloud data data between data users. Although developed primarily for exchange of lidar point cloud data, this format supports the exchange of any 3-dimensional x,y,z tuplet. This binary file format is an alternative to proprietary systems or a generic ASCII file interchange system used by many companies. The problem with proprietary systems is obvious in that data cannot be easily taken from one system to another. There are two major problems with the ASCII file interchange. The first problem is performance because the reading and interpretation of ASCII elevation data can be very slow and the file size can be extremely large even for small amounts of data. The second problem is that all information specific to the lidar data is lost. The LAS file format is a binary file format that maintains information specific to the lidar nature of the data while not being overly complex.
- 3D point cloud - dimensional coordinates system.. Point clouds can be produced directly by 3D scanner which records a large number of points returned from the external surfaces of objects or earth surface. These data are exchanged between LiDAR users mainly through LAS format files (.las).
- ArcGIS Desktop - effective desktop geographic information system (GIS) software. It is the essential software package for GIS professionals. ArcGIS Desktop users can create, analyze, manage, and share geographic information so decision-makers can make intelligent, informed decisions.
- USGS 3DEP Lidar Point Cloud Now Available as Amazon Public Dataset
- National Geospatial Program
- USGS Lidar Base Specification(LBS) online edition
- National Map Data Download and Visualization Services
- Light Detection and Ranging (lidar) - resolution models of ground elevation with a vertical accuracy of 10 centimeters (4 inches). Lidar equipment, which includes a laser scanner, a Global Positioning System (GPS), and an Inertial Navigation System (INS), is typically mounted on a small aircraft. The laser scanner transmits brief pulses of light to the ground surface. Those pulses are reflected or scattered back and their travel time is used to calculate the distance between the laser scanner and the ground.  Lidar data is initially collected as a “point cloud” of individual points reflected from everything on the surface, including structures and vegetation. To produce a “bare earth” Digital Elevation Model (DEM), structures and vegetation are stripped away.
- USGS 3DEP Lidar Point Cloud Now Available as Amazon Public Dataset
Robotics Learning Resources
- Language Understanding (LUIS) for Azure Cognitive Services
- Free Online AI & Robotics Courses
- REC Foundation Robotics Industry Certification
- Carnegie Mellon Robotics Academy
- RIA Robotic Integrator Certification Program
- AWS RoboMaker – Develop, Test, Deploy, and Manage Intelligent Robotics Apps
- Microsoft AI School
- Windows ML ROS Node
- Azure VM templates to bootstrap ROS and ROS 2 environments
- Google Robotics Research
- AWS RoboMaker – Develop, Test, Deploy, and Manage Intelligent Robotics Apps
- Windows ML ROS Node
- Microsoft AI School
- AWS RoboMaker – Develop, Test, Deploy, and Manage Intelligent Robotics Apps
- Top Robotics Courses Online from Udemy
- RIA Robotic Integrator Certification Program
- Windows ML ROS Node
- Azure VM templates to bootstrap ROS and ROS 2 environments
- Learn Robotics with Online Courses and Classes from edX
- Language Understanding (LUIS) for Azure Cognitive Services
- Carnegie Mellon Robotics Academy
- ROS on Windows 10
- Learn Robotics with Online Courses and Classes from edX
- RIA Robotic Integrator Certification Program
Robotic Arms & Dev Kits
- Checkout the AWS DeepRacer autonomous Car Kit
- Checkout the SunFounder PiCar-V Kit V2.0 for Raspberry Pi
- Automata
- Checkout the AWS DeepRacer autonomous Car Kit
- Checkout the SunFounder PiCar-V Kit V2.0 for Raspberry Pi
- Automata
Robotics Tools and Frameworks
- - Robot Framework - readable keywords. Its capabilities can be extended by libraries implemented with Python or Java.
  - The Robotics Library (RL) - contained C++ library for robot kinematics, motion planning and control. It covers mathematics, kinematics and dynamics, hardware abstraction, motion planning, collision detection, and visualization.RL runs on many different systems, including Linux, macOS, and Windows. It uses CMake as a build system and can be compiled with Clang, GCC, and Visual Studio.
  - Robot Structural Analysis Professional - integrated workflows to exchange data with Revit. It can help you to create more resilient, constructible designs that are accurate, coordinated, and connected to BIM.
  - ROS - level device control, implementation of commonly used functionality, message-passing between processes, and package management.
  - ROS2 - of-the-art algorithms, and with powerful developer tools, ROS has what you need for your next robotics project. And it’s all open source.
  - MoveIt - to-use robotics platform for developing advanced applications, evaluating new designs and building integrated products for industrial, commercial, R&D, and other domains.
  - Gazebo - quality graphics, and programmatic and graphical interfaces.
  - Intel Robot DevKit
  - Arduino - source platform used for building electronics projects. Arduino consists of both a physical programmable circuit board (often referred to as a microcontroller) and a piece of software, or IDE (Integrated Development Environment) that runs on your computer, used to write and upload computer code to the physical board.
  - ArduPilot
  - Light Detection and Ranging(LiDAR) - driving vehicles.
  - ROS-Industrial
  - AWS RoboMaker - managed, scalable infrastructure for simulation that customers use for multi-robot simulation and CI/CD integration with regression testing in simulation.
  - Azure Kinect ROS Driver - us/services/kinect-dk/) to the [Robot Operating System (ROS)](http://www.ros.org/). Developers working with ROS can use this node to connect an Azure Kinect Developer Kit to an existing ROS installation.
  - Azure IoT Hub for ROS
  - Azure Cognitive Services LUIS ROS Node
  - Linear algebra
  - Linear Algebra - Online Courses | Harvard University
  - Linear Algebra | MIT Open Learning Library
  - Linear Algebra - Khan Academy
  - Top Linear Algebra Courses on Coursera
  - Mathematics for Machine Learning: Linear Algebra on Coursera
  - Top Linear Algebra Courses on Udemy
  - Learn Linear Algebra with Online Courses and Classes on edX
  - The Math of Data Science: Linear Algebra Course on edX
  - Linear Algebra in Twenty Five Lectures | UC Davis
  - Linear Algebra | UC San Diego Extension
  - Linear Algebra for Machine Learning | UC San Diego Extension
  - Introduction to Linear Algebra, Interactive Online Video | Wolfram
  - Linear Algebra Resources | Dartmouth
  - ROS - level device control, implementation of commonly used functionality, message-passing between processes, and package management.
  - ArduPilot
  - ROS-Industrial
  - Microsoft Robotics Developer Studio - based programming environment for building robotics applications.
  - Linear Algebra - Khan Academy
  - Learn Linear Algebra with Online Courses and Classes on edX
  - Linear Algebra in Twenty Five Lectures | UC Davis
  - Linear Algebra Resources | Dartmouth
  - AutoGluon - to-machine-learning) that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.
  - Azure Cognitive Services LUIS ROS Node
  - Linear algebra
  - Linear Algebra - Online Courses | Harvard University
  - Linear Algebra | MIT Open Learning Library
  - Top Linear Algebra Courses on Coursera
  - Mathematics for Machine Learning: Linear Algebra on Coursera
  - Learn Linear Algebra with Online Courses and Classes on edX
  - AliceVision - of-the-art computer vision algorithms that can be tested, analyzed and reused. The project is a result of collaboration between academia and industry to provide cutting-edge algorithms with the robustness and the quality required for production usage.
  - The Robotics Library (RL) - contained C++ library for robot kinematics, motion planning and control. It covers mathematics, kinematics and dynamics, hardware abstraction, motion planning, collision detection, and visualization.RL runs on many different systems, including Linux, macOS, and Windows. It uses CMake as a build system and can be compiled with Clang, GCC, and Visual Studio.
  - Intel Robot DevKit
  - AirSim - source, cross platform, and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations.
  - The JPL Open Source Rover
  - CARLA - source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous 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.
  - ROS bridge
  - Visual Studio Code Extension for ROS
  - Azure Kinect ROS Driver - us/services/kinect-dk/) to the [Robot Operating System (ROS)](http://www.ros.org/). Developers working with ROS can use this node to connect an Azure Kinect Developer Kit to an existing ROS installation.
  - Azure IoT Hub for ROS
  - ROS 2 with ONNX Runtime
  - Azure Cognitive Services LUIS ROS Node
  - PowerMill - to-use tools for offline programming of robots. Get tools to help you optimize robotic paths and simulate virtual mock-ups of manufacturing cells and systems.
- i. Vector operations
  - slideserve
  - pinterest
- ii. Matrix operations
  - DPS Concepts
  - DPS Concepts
- iii. Matrix-vector product
- iv. Linear transformations
- v. Fundamental vector spaces
  - wikimedia
  - wolfram
- i. Solving systems of equations
  - slideshare
- ii. Systems of equations as matrix equations
- ii. Using elementary matrices
  - SDSU Physics
- iii. Transpose of a Matrix
  - slideserve
  - slideserve
- i. Basis
- ii. Matrix representations of linear transformations
- iii. Dimension and Basis for Vector Spaces
- iv. Row space, columns space, and rank of a matrix
- vi. Determinants
- viii. Linear Regression
  - Linear regression
  - Medium
  - Linear regression
  - Medium
  - Linear regression
  - Medium
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- i. Using row operations
  - YouTube
  - YouTube
- vii. Eigenvalues and eigenvectors
  - YouTube
  - YouTube
- v. Invertible matrix theorem
  - SDSU Physics
C/C++ Learning Resources
- viii. Linear Regression
  - C++ - platform language that can be used to build high-performance applications developed by Bjarne Stroustrup, as an extension to the C language.
  - C - purpose, high-level language that was originally developed by Dennis M. Ritchie to develop the UNIX operating system at Bell Labs. It supports structured programming, lexical variable scope, and recursion, with a static type system. C also provides constructs that map efficiently to typical machine instructions, which makes it one was of the most widely used programming languages today.
  - Embedded C - committee) to address issues that exist between C extensions for different [embedded systems](https://en.wikipedia.org/wiki/Embedded_system). The extensions hep enhance microprocessor features such as fixed-point arithmetic, multiple distinct memory banks, and basic I/O operations. This makes Embedded C the most popular embedded software language in the world.
  - C & C++ Developer Tools from JetBrains
  - Open source C++ libraries on cppreference.com
  - C++ Graphics libraries
  - C++ Libraries in MATLAB
  - C++ Tools and Libraries Articles
  - Google C++ Style Guide
  - Introduction C++ Education course on Google Developers
  - C++ style guide for Fuchsia
  - C and C++ Coding Style Guide by OpenTitan
  - Chromium C++ Style Guide
  - C++ Core Guidelines
  - C++ Style Guide for ROS
  - Learn C++
  - Learn C : An Interactive C Tutorial
  - C++ Institute
  - C++ Online Training Courses on LinkedIn Learning
  - C++ Tutorials on W3Schools
  - Learn C Programming Online Courses on edX
  - Learn C++ with Online Courses on edX
  - Learn C++ on Codecademy
  - Coding for Everyone: C and C++ course on Coursera
  - C++ For C Programmers on Coursera
  - Top C Courses on Coursera
  - C++ Online Courses on Udemy
  - Top C Courses on Udemy
  - Basics of Embedded C Programming for Beginners on Udemy
  - C++ For Programmers Course on Udacity
  - C++ Fundamentals Course on Pluralsight
  - Introduction to C++ on MIT Free Online Course Materials
  - Introduction to C++ for Programmers | Harvard
  - Online C Courses | Harvard University
Python Learning Resources
- viii. Linear Regression
  - PCEP – Certified Entry-Level Python Programmer certification
  - Python - level programming language. Python is used heavily in the fields of Data Science and Machine Learning.
  - Python Developer’s Guide
  - Azure Functions Python developer guide - us/azure/azure-functions/functions-reference).
  - CheckiO
  - Python Institute
  - PCAP – Certified Associate in Python Programming certification
  - MTA: Introduction to Programming Using Python Certification
  - Getting Started with Python in Visual Studio Code
  - Google's Python Style Guide
  - Google's Python Education Class
  - Real Python
  - Intro to Python for Data Science
  - Intro to Python by W3schools
  - Codecademy's Python 3 course
  - Learn Python with Online Courses and Classes from edX
  - Python Courses Online from Coursera
  - PCPP – Certified Professional in Python Programming 2
C/C++ Tools and Frameworks
- viii. Linear Regression
  - AWS SDK for C++
  - Visual Studio - rich application that can be used for many aspects of software development. Visual Studio makes it easy to edit, debug, build, and publish your app. By using Microsoft software development platforms such as Windows API, Windows Forms, Windows Presentation Foundation, and Windows Store.
  - Visual Studio Code
  - ReSharper C++
  - AppCode - fixes to resolve them automatically. AppCode provides lots of code inspections for Objective-C, Swift, C/C++, and a number of code inspections for other supported languages. All code inspections are run on the fly.
  - CLion - platform IDE for C and C++ developers developed by JetBrains.
  - Code::Blocks
  - Conan
  - High Performance Computing (HPC) SDK
  - Boost - edge C++. Boost has been a participant in the annual Google Summer of Code since 2007, in which students develop their skills by working on Boost Library development.
  - Automake
  - Cmake - source, cross-platform family of tools designed to build, test and package software. CMake is used to control the software compilation process using simple platform and compiler independent configuration files, and generate native makefiles and workspaces that can be used in the compiler environment of your choice.
  - GDB
  - GCC - C, Fortran, Ada, Go, and D, as well as libraries for these languages.
  - GSL - squares fitting. There are over 1000 functions in total with an extensive test suite.
  - OpenGL Extension Wrangler Library (GLEW) - platform open-source C/C++ extension loading library. GLEW provides efficient run-time mechanisms for determining which OpenGL extensions are supported on the target platform.
  - Libtool
  - Maven
  - TAU (Tuning And Analysis Utilities) - based sampling. All C++ language features are supported including templates and namespaces.
  - Clang - C, C++ and Objective-C++ compiler when targeting X86-32, X86-64, and ARM (other targets may have caveats, but are usually easy to fix). Clang is used in production to build performance-critical software like Google Chrome or Firefox.
  - OpenCV - time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
  - Libcu++
  - ANTLR (ANother Tool for Language Recognition)
  - Oat++ - efficient web application. It's zero-dependency and easy-portable.
  - Cython
  - Infer - C, and C. Infer is written in [OCaml](https://ocaml.org/).
Julia Tools, Libraries and Frameworks
- viii. Linear Regression
  - Julia for VSCode
  - JuliaPro
  - Juno
  - Profile (Stdlib)
  - JuliaGPU - level syntax and flexible compiler, Julia is well positioned to productively program hardware accelerators like GPUs without sacrificing performance.
  - CUDA.jl - friendly array abstraction, a compiler for writing CUDA kernels in Julia, and wrappers for various CUDA libraries.
  - JuMP.jl - specific modeling language for [mathematical optimization](https://en.wikipedia.org/wiki/Mathematical_optimization) embedded in Julia.
  - Knet
  - DataFrames.jl
  - Flux.jl - Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support.
R Learning Resources
- viii. Linear Regression
Python Frameworks, Libraries, and Tools
- viii. Linear Regression
  - TurboGears
  - Python Package Index (PyPI)
  - PyCharm
  - Django - level Python Web framework that encourages rapid development and clean, pragmatic design.
  - Flask
  - Web2py - source web application framework written in Python allowing allows web developers to program dynamic web content. One web2py instance can run multiple web sites using different databases.
  - Tornado - blocking network I/O, which can scale to tens of thousands of open connections.
  - HTTPie
  - Scrapy - level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.
  - Sentry
  - CherryPy - oriented HTTP web framework.
  - Sanic
  - Pyramid - world web application development and deployment more fun and more productive.
  - Falcon - performance Python web framework for building large-scale app backends and microservices with support for MongoDB, Pluggable Applications and autogenerated Admin.
  - NumPy
  - Pillow
  - IPython
  - GraphLab Create - scale, high-performance machine learning models.
  - Pandas
  - Matplotlib - quality figures in a variety of hardcopy formats and interactive environments across platforms.
  - Python Tools for Visual Studio(PTVS)
R Tools, Libraries, and Frameworks
- viii. Linear Regression
  - RStudio - highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management.
  - Shiny
  - Plotly
  - Metaflow - life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.
  - Prophet - linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data.
  - LightGBM
  - MLR
  - Plumber
  - Drake - focused pipeline toolkit for reproducibility and high-performance computing.
  - DiagrammeR
  - Knitr - purpose literate programming engine in R, with lightweight API's designed to give users full control of the output without heavy coding work.
  - Broom
  - Shiny
  - Dash
  - Rmarkdown
Julia Learning Resources
- viii. Linear Regression
  - Julia - level, [high-performance](https://julialang.org/benchmarks/) dynamic language for technical computing. Julia programs compile to efficient native code for [multiple platforms](https://julialang.org/downloads/#support_tiers) via LLVM.
  - JuliaHub
  - Julia Observer
  - Julia Manual
  - JuliaLang Essentials
  - Julia Style Guide
  - Julia By Example
  - JuliaLang Gitter
  - Julia Academy
  - Julia Meetup groups
  - Julia on Microsoft Azure

Programming Languages

C++ 16 Python 5 MATLAB 3 Scala 2 Shell 2 C# 1 Rust 1 TypeScript 1 Prolog 1 Dockerfile 1

Ecosyste.ms: Awesome

Autonomous-Systems-Guide

Computer Vision Learning Resources

Autodesk Tools and Frameworks

Computer Vision Tools, Libraries, and Frameworks

CUDA Tools Libraries, and Frameworks

MATLAB Learning Resources

Learning Resources for ML

ML Frameworks, Libraries, and Tools

CUDA Learning Resources

MATLAB Tools, Libraries, Frameworks

Photogrammetry Tools, Libraries, and Frameworks

Photogrammetry Learning Resources

Autodesk Learning Resources

LiDAR Learning Resources

LiDAR Tools & Frameworks

Robotics Learning Resources

Robotic Arms & Dev Kits

Robotics Tools and Frameworks

i. Vector operations

ii. Matrix operations

iii. Matrix-vector product

iv. Linear transformations

v. Fundamental vector spaces

i. Solving systems of equations

ii. Systems of equations as matrix equations

ii. Using elementary matrices

iii. Transpose of a Matrix

i. Basis

ii. Matrix representations of linear transformations

iii. Dimension and Basis for Vector Spaces

iv. Row space, columns space, and rank of a matrix

vi. Determinants

viii. Linear Regression

i. Using row operations

vii. Eigenvalues and eigenvectors

v. Invertible matrix theorem

C/C++ Learning Resources

viii. Linear Regression

Python Learning Resources

viii. Linear Regression

C/C++ Tools and Frameworks

viii. Linear Regression

Julia Tools, Libraries and Frameworks

viii. Linear Regression

R Learning Resources

viii. Linear Regression

Python Frameworks, Libraries, and Tools

viii. Linear Regression

R Tools, Libraries, and Frameworks

viii. Linear Regression

Julia Learning Resources

viii. Linear Regression