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手机应用软件:Robotics Engineering - Apps on Google Play

This Robotics Engineering App provides the basic know-how on the foundations of robotics: modelling, planning and control. The App takes the user through a step-by step design process in this rapidly advancing specialty area of robot design.This App provides the professional engineer and student with important and detailed methods and examples ...

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GitHub+awesome

在github中搜索awesome+关键词,可以看到非常多有用的资源。

编程基础部分:

Matlab:https://github.com/uhub/awesome-matlab

Python:https://github.com/vinta/awesome-python

C++:https://github.com/fffaraz/awesome-cpp

如,机器人学:https://github.com/kiloreux/awesome-robotics

This is a list of various books, courses and other resources for robotics. It's an attempt to gather useful material in one place for everybody who wants to learn more about the field.

CoursesBooksSoftware and Libraries

Gazebo Robot Simulator

ROS The Robot Operating System (ROS) is a flexible framework for writing robot software. It is a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust robot behavior across a wide variety of robotic platforms.

ROS2 ROS2 is a new version of ROS with radical design changes and improvement over older ROS version.

RobWork RobWork is a collection of C++ libraries for simulation and control of robot systems. RobWork is used for research and education as well as for practical robot applications.

MRPT Mobile Robot Programming Toolkit provides developers with portable and well-tested applications and libraries covering data structures and algorithms employed in common robotics research areas.

Robotics Library The Robotics Library (RL) is a self-contained C++ library for robot kinematics, motion planning and control. It covers mathematics, kinematics and dynamics, hardware abstraction, motion planning, collision detection, and visualization.

Simbad 2D/3D simulator in Java and Jython.

Morse General purpose indoor/outdoor 3D simulator.

Carmen CARMEN is an open-source collection of software for mobile robot control. CARMEN is modular software designed to provide basic navigation primitives including: base and sensor control, logging, obstacle avoidance, localization, path planning, and mapping.

Peekabot Peekabot is a real-time, networked 3D visualization tool for robotics, written in C++. Its purpose is to simplify the visualization needs faced by a roboticist daily.

YARP Yet Another Robot Platform.

V-REP Robot simulator, 3D, source available, Lua scripting, APIs for C/C++, Python, Java, Matlab, URBI, 2 physics engines, full kinematic solver.

Webots Webots is a development environment used to model, program and simulate mobile robots.

Drake A planning, control and analysis toolbox for nonlinear dynamical systems.

Neurorobotics Platform (NRP) An Internet-accessible simulation system that allows the simulation of robots controlled by spiking neural networks.

The Player Project Free Software tools for robot and sensor applications

Open AI's Roboschool Open-source software for robot simulation, integrated with OpenAI Gym.

ViSP Open-source visual servoing platform library, is able to compute control laws that can be applied to robotic systems.

ROS Behavior Trees Open-source library to create robot's behaviors in form of Behavior Trees running in ROS (Robot Operating System).

PapersConferencesJournalsCompetitionsCompanies
  • Boston Dynamics robotics R&D company, creator of the state of the art Atlas and Spot robots
  • iRobot manufacturer of the famous Roomba robotic vacuum cleaner
  • PAL Robotics
  • Aldebaran Robotics creator of the NAO robot
  • ABB Robotics the largest manufacturer of industrial robots
  • KUKA Robotics major manufacturer of industrial robots targeted at factory automation
  • FANUC industrial robots manufacturer with the biggest install base
  • Rethink Robotics creator of the collaborative robot Baxter
  • DJI industry leader in drones for both commerical and industrial needs.
  • The construct sim A cloud based tool for building modern, future-proof robot simulations.
  • Fetch Robotics A robotics startup in San Jose, CA building the future of e-commerce fulfillment and R&D robots.
  • Festo Robotics Festo is known for making moving robots that move like animals such as the sea gull like SmartBird, jellyfish, butterflies and kangaroos.
MiscRelated awesome lists

Awesome links, software libraries, papers, and other intersting links that are useful for robots.

Relevant Awesome ListsSimulators
  • V-REP - Create, Simulate, any Robot.
  • Microsoft Airsim - Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research.
  • Bullet Physics SDK - Real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc. Also see pybullet.
Visualization, Video, Display, and Rendering
  • Pangolin - A lightweight portable rapid development library for managing OpenGL display / interaction and abstracting video input.
  • PlotJuggler - Quickly plot and re-plot data on the fly! Includes optional ROS integration.
  • Data Visualization - A list of awesome data visualization tools.
Machine LearningTensorFlow related
  • Keras - Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano.
  • keras-contrib - Keras community contributions.
  • TensorFlow - An open-source software library for Machine Intelligence.
  • recurrentshop - Framework for building complex recurrent neural networks with Keras.
  • tensorpack - Neural Network Toolbox on TensorFlow.
  • tensorlayer - Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
  • TensorFlow-Examples - TensorFlow Tutorial and Examples for beginners.
  • hyperas - Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization.
  • elephas - Distributed Deep learning with Keras & Spark
  • PipelineAI - End-to-End ML and AI Platform for Real-time Spark and Tensorflow Data Pipelines.
  • sonnet - Google Deepmind APIs on top of TensorFlow.
  • visipedia/tfrecords - Demonstrates the use of TensorFlow's TFRecord data format.

Image Segmentation

Logging and Messaging
  • spdlog - Super fast C++ logging library.
  • lcm - Lightweight Communications and Marshalling, message passing and data marshalling for real-time systems where high-bandwidth and low latency are critical.
Tracking
  • simtrack - A simulation-based framework for tracking.
  • ar_track_alvar - AR tag tracking library for ROS.
  • artoolkit5 - Augmented Reality Toolkit, which has excellent AR tag tracking software.
Robot Operating System (ROS)
  • ROS - Main ROS website.
  • ros2/design - Design documentation for ROS 2.0 effort.
Kinematics, Dynamics, Constrained Optimization
  • jrl-umi3218/Tasks - Tasks is library for real time control of robots and kinematic trees using constrained optimization.
  • jrl-umi3218/RBDyn - RBDyn provides a set of classes and functions to model the dynamics of rigid body systems.
  • ceres-solver - Solve Non-linear Least Squares problems with bounds constraints and general unconstrained optimization problems. Used in production at Google since 2010.
  • orocos_kinematics_dynamics - Orocos Kinematics and Dynamics C++ library.
  • flexible-collsion-library - Performs three types of proximity queries on a pair of geometric models composed of triangles, integrated with ROS.
  • robot_calibration - generic robot kinematics calibration for ROS
CalibrationReinforcement LearningDrivers for Sensors, Devices and Arms
  • libfreenect2 - Open source drivers for the Kinect for Windows v2 and Xbox One devices.
  • iai_kinect2 - Tools for using the Kinect One (Kinect v2) in ROS.
  • grl - Generic Robotics Library: Cross platform drivers for Kuka iiwa and Atracsys FusionTrack with optional v-rep and ros drivers. Also has cross platform Hand Eye Calibration and Tool Tip Calibration.
Datasets
  • pascal voc 2012 - The classic reference image segmentation dataset.
  • openimages - Huge imagenet style dataset by Google.
  • COCO - Objects with segmentation, keypoints, and links to many other external datasets.
  • cocostuff - COCO additional full scene segmentation including backgrounds and annotator.
  • Google Brain Robot Data - Robotics datasets including grasping, pushing, and pouring.
  • Materials in Context - Materials Dataset with real world images in 23 categories.
  • Dex-Net 2.0 - 6.7 million pairs of synthetic point clouds and grasps with robustness labels.

Dataset Collection

  • cocostuff - COCO additional full scene segmentation including backgrounds and annotator.
Linear Algebra & Geometry
  • Eigen - Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.
  • Boost.QVM - Quaternions, Vectors, Matrices library for Boost.
  • Boost.Geometry - Boost.Geometry contains instantiable geometry classes, but library users can also use their own.
  • SpaceVecAlg - Implementation of spatial vector algebra for 3D geometry with the Eigen3 linear algebra library.
  • Sophus - C++ implementation of Lie Groups which are for 3D Geometry, using Eigen.
Point Clouds
  • libpointmatcher - An "Iterative Closest Point" library robotics and 2-D/3-D mapping.
  • Point Cloud Library (pcl) - The Point Cloud Library (PCL) is a standalone, large scale, open project for 2D/3D image and point cloud processing.
Simultaneous Localization and Mapping (SLAM)
  • ElasticFusion - Real-time dense visual SLAM system.
  • co-fusion - Real-time Segmentation, Tracking and Fusion of Multiple Objects. Extends ElasticFusion.
  • Google Cartographer - Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.
  • OctoMap - An Efficient Probabilistic 3D Mapping Framework Based on Octrees. Contains the main OctoMap library, the viewer octovis, and dynamicEDT3D.
  • ORB_SLAM2 - Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities.
The list of vision-based SLAM / Visual Odometry open source projects, libraries, dataset, tools, and studiesIndexLibraries

Basic vision and trasformation libraries

Thread-safe queue libraries

Loop detection

Graph Optimization

Map library

Dataset

Dataset for benchmark/test/experiment/evalutation

ToolsProjects

RGB (Monocular):

[1] Georg Klein and David Murray, "Parallel Tracking and Mapping for Small AR Workspaces", Proc. ISMAR 2007 [2] Georg Klein and David Murray, "Improving the Agility of Keyframe-based SLAM", Proc. ECCV 2008

  • DSO. Available on ROS

Direct Sparse Odometry, J. Engel, V. Koltun, D. Cremers, In arXiv:1607.02565, 2016 A Photometrically Calibrated Benchmark For Monocular Visual Odometry, J. Engel, V. Usenko, D. Cremers, In arXiv:1607.02555, 2016

LSD-SLAM: Large-Scale Direct Monocular SLAM, J. Engel, T. Schöps, D. Cremers, ECCV '14 Semi-Dense Visual Odometry for a Monocular Camera, J. Engel, J. Sturm, D. Cremers, ICCV '13

[1] Raúl Mur-Artal, J. M. M. Montiel and Juan D. Tardós. ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE > Transactions on Robotics, vol. 31, no. 5, pp. 1147-1163, 2015. (2015 IEEE Transactions on Robotics Best Paper Award). PDF. [2] Dorian Gálvez-López and Juan D. Tardós. Bags of Binary Words for Fast Place Recognition in Image Sequences. IEEE > Transactions on Robotics, vol. 28, no. 5, pp. 1188-1197, 2012. PDF.

D. Nister, “An efficient solution to the five-point relative pose problem,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 26, no. 6, pp. 756–770, 2004.

Christian Forster, Matia Pizzoli, Davide Scaramuzza, "SVO: Fast Semi-direct Monocular Visual Odometry," IEEE International Conference on Robotics and Automation, 2014.

RGB and Depth (Called RGBD):

Real-Time Visual Odometry from Dense RGB-D Images, F. Steinbucker, J. Strum, D. Cremers, ICCV, 2011

[1]Dense Visual SLAM for RGB-D Cameras (C. Kerl, J. Sturm, D. Cremers), In Proc. of the Int. Conf. on Intelligent Robot Systems (IROS), 2013. [2]Robust Odometry Estimation for RGB-D Cameras (C. Kerl, J. Sturm, D. Cremers), In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), 2013 [3]Real-Time Visual Odometry from Dense RGB-D Images (F. Steinbruecker, J. Sturm, D. Cremers), In Workshop on Live Dense Reconstruction with Moving Cameras at the Intl. Conf. on Computer Vision (ICCV), 2011.

Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM, 2014 Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation, 2013

[1] Raúl Mur-Artal, J. M. M. Montiel and Juan D. Tardós. ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE > Transactions on Robotics, vol. 31, no. 5, pp. 1147-1163, 2015. (2015 IEEE Transactions on Robotics Best Paper Award). [2] Dorian Gálvez-López and Juan D. Tardós. Bags of Binary Words for Fast Place Recognition in Image Sequences. IEEE Transactions on Robotics, vol. 28, no. 5, pp. 1188-1197, 2012.

Kahler, O. and Prisacariu, V.~A. and Ren, C.~Y. and Sun, X. and Torr, P.~H.~S and Murray, D.~W. Very High Frame Rate Volumetric Integration of Depth Images on Mobile Device. IEEE Transactions on Visualization and Computer Graphics (Proceedings International Symposium on Mixed and Augmented Reality 2015

Real-time Large Scale Dense RGB-D SLAM with Volumetric Fusion, T. Whelan, M. Kaess, H. Johannsson, M.F. Fallon, J. J. Leonard and J.B. McDonald, IJRR '14

[1] ElasticFusion: Real-Time Dense SLAM and Light Source Estimation, T. Whelan, R. F. Salas-Moreno, B. Glocker, A. J. Davison and S. Leutenegger, IJRR '16 [2] ElasticFusion: Dense SLAM Without A Pose Graph, T. Whelan, S. Leutenegger, R. F. Salas-Moreno, B. Glocker and A. J. Davison, RSS '15

Martin Rünz and Lourdes Agapito. Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects. 2017 IEEE International Conference on Robotics and Automation (ICRA)

RGBD and LIDAR:

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awesome-deep-vision-web-demo

A curated list of awesome deep vision web demo

Contributing

Please feel free to pull requests to add papers.

Vision Demo ListHand-written Digit RecognitionImage SegmentationImage ClassificationObject DetectionText DetectionAge EstimationAutoEncoderGANStyle TransferImage TranslationColorizationImage CaptioningVisual Q&AGoogle's AI ExperimentGaze ManipulationSuper-ResolutionSaliency MapFont GenerationImage to ASCII CodeImage CompletionOCR (Optical Character Recognition)Human Pose EstimationOthersNeural-Net DemoText To SpeechSpeech Noise ReductionSinging GenerationSound Synthesizer

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量子机器学习-量子机器人

The computing field must have a change from classical to quantum.
计算领域必须从经典变为量子。
https://github.com/krishnakumars ... um-machine-learning

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Fall 2017 - Vision Algorithms for Mobile Robotics

UZH-BMINF020 / ETH-151-0632-00L


The course is open to all the students of the University of Zurich and ETH. Students should register through their own institutions.


Goal of the Course


For a robot to be autonomous, it has to perceive and understand the world around it. This course introduces you to the key computer vision algorithms used in mobile robotics, such as feature extraction, multiple view geometry, dense reconstruction, tracking, image retrieval, event-based vision, and visual-inertial odometry (the algorithms behind Google Tango, Apple ARKit, Google ARCore, Microsoft Hololens, Magic Leap and the Mars rovers). Basics knowledge of algebra, geomertry, and matrix calculus are required.


Time and location


Lectures: every Thursday from 10:15 to 12:00 in ETH LFW C5, Universitätstrasse 2, 8092 Zurich.

Exercises: Thursdays, roughly every two weeks, from 13:15 to 15:00 in ETH HG E 1.1, Rämistrasse 101, 8092 Zurich.

Please check out the course agenda below for the exact schedule.


Course Program, Slides, and Add-on Material


Official course program (please notice that this is a tentative schedule and that the effective content of the lecture can change from week to week.

Date

Lecture and Exercise Title

Slides and add-on material

21.09.2017Lecture 01 - Introduction to Computer Vision and Visual OdometrySlides (last update 21.09.2017)
Visual odometry tutorial Part I
Visual odometry tutorial Part II
SLAM survey paper
28.09.2017Lecture 02 - Image Formation 1: perspective projection and camera modelsSlides (last update 27.09.2017)
05.10.2017Lecture 03 - Image Formation 2: camera calibration algorithms
Exercise 01 - Augmented reality wireframe cube
Slides (last update 04.10.2017)
Additional reading on P3P and PnP problems
Exercise 01 (last update 04.10.2017)
Solutions (last update 12.10.2017)
Introduction to Matlab
12.10.2017Lecture 04 - Filtering & Edge detection
Exercise 02 - PnP problem
Slides (last update 12.10.2017)
Exercise 02 (last update 12.10.2017)
Solutions (last update 16.10.2017)
19.10.2017Lecture 05 - Point Feature Detectors, Part 1
Exercise 03 - Harris detector + descriptor + matching
Slides (last update 19.10.2017)
Exercise 03 (last update 17.10.2017)
Solutions (last update 24.10.2017)
26.10.2017Lecture 06 - Point Feature Detectors, Part 2Slides (last update 26.10.2017)
Additional reading on feature detection
02.11.2017Lecture 07 - Multiple-view geometry 1
Exercise 04 - Stereo vision: rectification, epipolar matching, disparity, triangulation
Slides (last update 01.11.2017)
Additional reading on stereo image rectification
Exercise 04(last update 31.10.2017)
Solutions (last update 31.10.2017)
09.11.2017Lecture 08 - Multiple-view geometry 2
Exercise 05 - Two-view Geometry
Slides (last update 9.11.2017)
Additional reading on 2-view geometry
Exercise 05 (last update 8.11.2017)
Solutions (last update 14.11.2017)
16.11.2017Lecture 09 - Multiple-view geometry 3
Exercise 06 - P3P algorithm and RANSAC
Slides (last update 22.11.2017)
Additional reading on open-source VO algorithms
Exercise 06 (last update 16.11.2017)
Solutions (last update 20.11.2017)
23.11.2017Lecture 10 - Dense 3D Reconstruction
Exercise session: Intermediate VO Integration
Slides (last update 29.11.2017)
Additional reading on dense 3D reconstruction
Find the VO project downloads below
30.11.2017Lecture 11 - Optical Flow and Tracking (Lucas-Kanade)
Exercise 07 - Lucas-Kanade tracker
Slides (last update 29.11.2017)
Additional reading on Lucas-Kanade
Exercise 07 (last update 30.11.2017)
Solutions (last update 06.12.2017)
07.12.2017Lecture 12 - Place recognition
Exercise session: Deep Learning Tutorial
Slides (last update 07.12.2017)
Additional reading on Bag-of-Words-based place recognition
Optional exercise on place recognition(last update 06.12.2017)
Deep Learning Slides(last update 07.12.2017)
14.12.2017Lecture 13 - Visual inertial fusion
Exercise 08 - Bundle Adjustment
Slides (last update 14.12.2017)
Advanced Slides for intrerested reader
Additional reading on visual-inertial fusion
Exercise 08 (last update 13.12.2017)
Solutions (last update 17.12.2017)
21.12.2017Lecture 14 - Event based vision + Scaramuzza's lab visit with live demos
Exercise session: final VO integration
Slides (last update 19.12.2017)
Additional reading on event-based vision

Oral Exam Questions (last udpate 21.12.2017)


The oral exam will last 30 minutes and will consist of one application question followed by two theoretical questions. This documentcontains a "non exhaustive" list of possible application questions and an "exhaustive" list of all the topics that you should learn about the course, which will be subject of discussion in the theoretical part.


Grading and optional Mini Project (last udpate 22.11.2017)


The final grade is based on the oral exam (30 minutes, exam date for UZH: Jan. 18; exam date for ETH students will be between January 22 and February 9 2018, dates communicated by ETH). Mini projects are optional and up to the students. Depending on the result of the mini project (see Project Specification in the table below), the student will be rewarded with a grade increase of up to 0.5 on the final grade. However, notice that the mini project can be quite time consuming. Mini project specification and files can be found in the table below. The deadline for the project is Sunday, 07.01.2018, 23:59:59, and it can be submitted via e-mail to the assistants (detailed instructions in specification).

DescriptionLink(size)
Project Specificationvo_project_statement.pdf (600 kB, last updated 22.11.2017)
FAQFrequently Asked Questions
Parking garage dataset (easy)parking.zip (208.3 MB)
KITTI 00 dataset (hard)kitti00.zip (2.3 GB)
Malaga 07 dataset (hard)malaga-urban-dataset-extract-07.zip (2.4 GB)
Matlab script to load datasetsmain.m (2.6 kB)

Recommended Textbooks

(All available in the NEBIS catalogue)


  • Robotics, Vision and Control: Fundamental Algorithms, 2nd Ed., by Peter Corke 2017. The PDF of the book can be freely downloaded (only with ETH VPN) from the author's webpage.
  • Computer Vision: Algorithms and Applications, by Richard Szeliski, Springer, 2010. The PDF of the book can be freely downloaded from the author's webpage.
  • An Invitation to 3D Vision, by Y. Ma, S. Soatto, J. Kosecka, S.S. Sastry.
  • Multiple view Geometry, by R. Hartley and A. Zisserman.
  • Chapter 4 of "Autonomous Mobile Robots", by R. Siegwart, I.R. Nourbakhsh, D. Scaramuzza. PDF

Spring 2015 - Autonomous Mobile Robots

The course is currently open to all the students of the University of Zurich and ETH (Bachelor's and Master's). Lectures take place every Monday (from 16.02.2014 to 30.05.2014) from 14:15 to 16:00 in the ETH main building (HG) in room E 1.2. Exercise take place almost every second Tuesday from 10:15 to 12:00 in the ETH main building in room G1.

The course is also given as an MOOC (Massive Open Online Course) under edX.

Course Program

Official course webpage.

Recommended Textbook

R. Siegwart, I.R. Nourbakhsh, and D. Scaramuzza

Introduction to autonomous mobile robots 2nd Edition (hardback)

A Bradford Book, The MIT Press, ISBN: 978-0-262-01535-6, February, 2011

The book can be bought during the first lecture or on Amazon.

MIT WebsiteBook WebsiteBuy

Archived slides, videos, and lecture recordings

Since 2007, Prof. Davide Scaramuzza has been teaching this course at ETH Zurich and since 2012 the course has been shared also with University of Zurich. The lectures are based on Prof. Scaramuzza's book Autonomous Mobile Robots, MIT Press. Recordings of previous lectures (until 2012) can be watched or downloaded, only by ETH students, here.

You can download all the lecture slides and videos of past lectures (updated in 2010) from the following links:

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