手機應用軟體：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中搜尋awesome+關鍵詞，可以看到非常多有用的資源。
Python +Robotics
https://github.com/AtsushiSakai/PythonRobotics
程式設計基礎部分：
Matlab：https://github.com/uhub/awesome-matlab
Python：https://github.com/vinta/awesome-python
C++：https://github.com/fffaraz/awesome-cpp
如，機器人學：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.
Artificial Intelligence for Robotics Udacity
Robotics Nanodegree Udacity
Autonomous Mobile Robots edX
Underactuated Robotics edX
Robot Mechanics and Control, Part I edX
Robot Mechanics and Control, Part II edX
Autonomous Navigation for Flying Robots edX
Robotics Micromasters edX
Robotics Specialization by GRASP Lab Coursera
Control of Mobile Robots Coursera
QUT Robot Academy QUT
Robotic vision QUT
Introduction to robotics MIT
Robotics: Vision Intelligence and Machine Learning edX
Applied robot design Stanford University
Introduction to Robotics Stanford University
Introduction to Mobile Robotics University of Freiburg
Robotics edx
Columbia Robotics edx
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents series)
Introduction to Autonomous Mobile Robots (Intelligent Robotics and Autonomous Agents series)
Springer Handbook of Robotics
Planning Algorithms
A gentle introduction to ROS
A Mathematical Introduction to Robotic Manipulation
Learning Computing With Robots
Robotics, Vision and Control: Fundamental Algorithms in MATLAB (Springer Tracts in Advanced Robotics)
INTECH Books
Introduction to Autonomous Robots
Principles of Robot Motion: Theory, Algorithms, and Implementations
Introduction to Modern Robotics: Mechanics, Planning, and Control [pdf]
Learning ROS for Robotics Programming
Mastering ROS for Robotics Programming
Behavior Trees in Robotics and AI: An Introduction [pdf]
Automated Planning and Acting [pdf]
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).
Optimization Based Controller Design and Implementation for the Atlas Robot in the DARPA Robotics Challenge Finals
ACM/IEEE International Conference on Human Robot Interaction (HRI)
CISM IFToMM Symposium on Robot Design, Dynamics and Control (RoManSy)
IEEE Conference on Decision and Controls (CDC)
IEEE International Conference on Rehabilitation Robotics (ICORR)
IEEE International Conference on Robotics and Automation (ICRA)
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
IEEE-RAS International Conference on Humanoid Robots (Humanoids)
International Symposium of Robotic Research (ISRR)
International Symposium of Experimental Robotics (ISER)
Robotica
Robotics: Science and Systems Conference (RSS)
The International Workshop on the Algorithmic Foundations of Robotics (WAFR)
Autonomous Robots
Bioinspiration & Biomimetics
Frontiers in Robotics and AI
IEEE Robotics & Automation Magazine
IEEE Transactions on Haptics
IEEE Transactions on Robotics
IEEE/ASME Transactions on Mechatronics
International Journal of Social Robotics
Journal of Field Robotics
Journal of Intelligent & Robotic Systems
Mechatronics
Robotics and Computer-Integrated Manufacturing
Robotics and Autonomous Systems
The International Journal of Robotics Research
ICRA Robot Challenges
RobotChallenge
DARPA Robotics Challenge
European Robotics Challenges
First Robotics Competition
VEX Robotics Competition
RoboCup
Eurobot International Students Robotics Contest
RoboMasters
RoboSoft, Grand Challenge
Intelligent Ground Vehicle Competition
Robotex The biggest robotics festival in Europe
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.
IEEE Spectrum Robotics robotics section of the IEEE Spectrum magazine
MIT Technology Review Robotics robotics section of the MIT Technology Review magazine
reddit robotics subreddit
RosCON conference (video talks included)
Carnegie Mellon Robotics Academy
Let's Make Robots
How do I learn Robotics?
Free NXT Lego MindStorms NXT-G code tutorials
StackExachange Robotics community
47 Programmable robotic kits
Awesome Artificial Intelligence
Awesome Computer Vision
Awesome Machine Learning
Awesome Deep Learning
Awesome Deep Vision
Awesome Reinforcement Learning
Awesome Robotics
Awesome Robotics Libraries
Awesome links, software libraries, papers, and other intersting links that are useful for robots.
Kiloreaux/awesome-robotics - Learn about Robotics.
Robotics Libraries - Another list of awesome robotics libraries.
Computer Vision
Deep Learning - Neural networks.
TensorFlow - Library for machine intelligence.
Papers - The most cited deep learning papers.
Deep Vision - Deep learning for computer vision
Data Visualization - See what your robot is doing with any programming language.
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.
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.
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
tf-image-segmentation - Image Segmentation framework based on Tensorflow and TF-Slim library.
Keras-FCN
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.
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.
ROS - Main ROS website.
ros2/design - Design documentation for ROS 2.0 effort.
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
handeye-calib-camodocal - generic robot hand-eye calibration.
kalibr - camera and imu calibration for ROS
TensorForce - A TensorFlow library for applied reinforcement learning
gqcnn - Grasp Quality Convolutional Neural Networks (GQ-CNNs) for grasp planning using training datasets from the Dexterity Network (Dex-Net)
Guided Policy Search - Guided policy search (gps) algorithm and LQG-based trajectory optimization, meant to help others understand, reuse, and build upon existing work.
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.
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
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.
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.
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.
Libraries
Dataset
Tools
Projects
Learn
Miscellaneous
Basic vision and trasformation libraries
OpenCV
Eigen
Sophus
ROS
PointCloud
Thread-safe queue libraries
concurrentqueue
Intel® TBB
Facebook folly PC
Loop detection
dorian3d
Graph Optimization
ceres-solver
g2o
gtasm
Vertigo
Map library
ETHZ ASL/Grip Map
OmniMapper
OctoMap
Dataset for benchmark/test/experiment/evalutation
TUM Universtiy
KTTI Vision benchmark
UNI-Freiburg
rgbd-dataset tool from TUM
evo - evaluation tool for different trajectory formats
RGB (Monocular):
PTAM
[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. Available on ROS
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
ORB-SLAM. Available on ROS
[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.
Nister's Five Point Algorithm for Essential Matrix estimation, and FAST features, with a KLT tracker
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.
SVO-SLAM. Available on ROS
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):
OpenCV RGBD-Odometry (Visual Odometry based RGB-D images)
Real-Time Visual Odometry from Dense RGB-D Images, F. Steinbucker, J. Strum, D. Cremers, ICCV, 2011
Dense Visual SLAM for RGB-D Cameras. Available on ROS
[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.
RTAB MAP - Real-Time Appearance-Based Mapping. Available on ROS
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
ORB2-SLAM. Available on ROS
[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.
InfiniTAM∞ v2
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
Kintinuous
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
ElasticFusion
[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
Co-Fusion
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:
Google's cartographer. Available on ROS
awesome-deep-vision-web-demo
A curated list of awesome deep vision web demo
Please feel free to pull requests to add papers.
https://tensorflow-mnist.herokuapp.com/
https://erkaman.github.io/regl-cnn/src/demo.html
https://transcranial.github.io/keras-js/#/mnist-cnn
CRF+RNN (ICCV 2015) http://www.robots.ox.ac.uk/~szheng/crfasrnndemo
VGG-16 https://deeplearning4j.org/demo-classifier-vgg16
Illustration2vec http://demo.illustration2vec.net/
Leiden Univ. http://goliath.liacs.nl/
Clarifai https://www.clarifai.com/demo
Google Colud Vision API http://vision-explorer.reactive.ai/#/?_k=aodf68
IBMWatson Vision API https://visual-recognition-demo.mybluemix.net/
Karpathy: MNIST ConvNet http://cs.stanford.edu/people/karpathy/convnetjs/demo/mnist.html
Karpathy: CIFAR10 ConvNet http://cs.stanford.edu/people/karpathy/convnetjs/demo/cifar10.html
keras-js: IMAGENET 50-layer Residual Network https://transcranial.github.io/keras-js/#/resnet50
keras-js: IMAGENET Inception-v3 https://transcranial.github.io/keras-js/#/inception-v3
keras-js: IMAGENET SqueezeNet v1.1 https://transcranial.github.io/keras-js/#/squeezenet-v1.1
Teachable Machine: 3 Classes with online video https://teachablemachine.withgoogle.com/
http://silverpond.com.au/object-detector
Single Shot Text Detector with Regional Attention (ICCV 2017) http://128.227.246.42:5555/
https://how-old.net/
VAE : MNIST Geneartation http://www.dpkingma.com/sgvb_mnist_demo/demo.html
VAE : keras-js MNIST https://transcranial.github.io/keras-js/#/mnist-vae
VAE : Gray Face Generation http://vdumoulin.github.io/morphing_faces/online_demo.html
Karpathy: Denoising AutoEncoder http://cs.stanford.edu/people/karpathy/convnetjs/demo/autoencoder.html
GAN : 1D Gaussian Distribution Fitting http://cs.stanford.edu/people/karpathy/gan/
DCGAN : Asian Color Face Generation http://carpedm20.github.io/faces/
DCGAN : Character Generation http://mattya.github.io/chainer-DCGAN/
ACGAN : keras-js MNIST https://transcranial.github.io/keras-js/#/mnist-acgan
Girls Chacacter Generation : http://make.girls.moe/#/
GAN-playground : https://reiinakano.github.io/gan-playground/
On/Off-line Style Transfer, Deep Dream https://deepdreamgenerator.com/gallery
Offline Style Transfer, http://demos.algorithmia.com/deep-style/
pix2pix https://affinelayer.com/pixsrv/index.html
pix2pix (human face) http://fotogenerator.npocloud.nl/
sketch to color image https://paintschainer.preferred.tech/
draw sketch and colorize it https://sketch.pixiv.net/draw
deepcolor http://color.kvfrans.com/
gray photo to color photo http://demos.algorithmia.com/colorize-photos/
MS https://www.captionbot.ai/
http://vqa.daylen.com/
https://cloudcv.org/vqa/
Quick Draw https://quickdraw.withgoogle.com/#
Auto Draw http://www.autodraw.com/
Sketch RNN (Draw together with a neural network) https://aiexperiments.withgoogle.com/sketch-rnn-demo
http://163.172.78.19/
http://waifu2x.udp.jp/
SALICON (ICCV 2015) http://salicon.net/demo/#
http://fontjoy.com/
https://art.pixlab.io/
http://jhyu.me/posts/2018/01/20/generative-inpainting.html
http://tesseract.projectnaptha.com/
https://control.kylemcdonald.net/posenet/
tensorflow play ground http://playground.tensorflow.org/
karpathy: toy 2d classification http://cs.stanford.edu/people/karpathy/convnetjs/demo/classify2d.html
Wavenet https://deepmind.com/blog/wavenet-generative-model-raw-audio/
SEGAN ('17.03) http://veu.talp.cat/segan/
Neural Singing ('17.04) http://www.dtic.upf.edu/~mblaauw/IS2017_NPSS/
Neural Synthesizer https://aiexperiments.withgoogle.com/sound-maker/view/
PythonRobotics
Python codes for robotics algorithm.
Table of Contents
What is this?
Requirements
How to use
Localization
Extended Kalman Filter localization
Unscented Kalman Filter localization
Particle filter localization
Histogram filter localization
Mapping
Gaussian grid map
Ray casting grid map
k-means object clustering
Object shape recognition using circle fitting
SLAM
Iterative Closest Point (ICP) Matching
EKF SLAM
FastSLAM 1.0
FastSLAM 2.0
Graph based SLAM
Path Planning
Dynamic Window Approach
Grid based search
Dijkstra algorithm
A* algorithm
Potential Field algorithm
Model Predictive Trajectory Generator
Path optimization sample
Lookup table generation sample
State Lattice Planning
Uniform polar sampling
Biased polar sampling
Lane sampling
Probabilistic Road-Map (PRM) planning
Voronoi Road-Map planning
Rapidly-Exploring Random Trees (RRT)
Basic RRT
RRT*
RRT with dubins path
RRT* with dubins path
RRT* with reeds-sheep path
Informed RRT*
Batch Informed RRT*
Closed Loop RRT*
LQR-RRT*
Cubic spline planning
B-Spline planning
Eta^3 Spline path planning
Bezier path planning
Quintic polynomials planning
Dubins path planning
Reeds Shepp planning
LQR based path planning
Optimal Trajectory in a Frenet Frame
Path tracking
Pure pursuit tracking
Stanley control
Rear wheel feedback control
Linear–quadratic regulator (LQR) steering control
Linear–quadratic regulator (LQR) speed and steering control
Model predictive speed and steering control
License
Contribution
Support
Authors
This is a Python code collection of robotics algorithms, especially for autonomous navigation.
Features:
Widely used and practical algorithms are selected.
Minimum dependency.
Easy to read for understanding each algorithm's basic idea.
Python 3.6.x
numpy
scipy
matplotlib
pandas
cvxpy
Install the required libraries.
Clone this repo.
Execute python script in each directory.
Add star to this repo if you like it .
This is a sensor fusion localization with Extended Kalman Filter(EKF).
The blue line is true trajectory, the black line is dead reckoning trajectory,
the green point is positioning observation (ex. GPS), and the red line is estimated trajectory with EKF.
The red ellipse is estimated covariance ellipse with EKF.
Ref:
PROBABILISTIC ROBOTICS
This is a sensor fusion localization with Unscented Kalman Filter(UKF).
The lines and points are same meaning of the EKF simulation.
Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization
This is a sensor fusion localization with Particle Filter(PF).
and the red line is estimated trajectory with PF.
It is assumed that the robot can measure a distance from landmarks (RFID).
This measurements are used for PF localization.
This is a 2D localization example with Histogram filter.
The red cross is true position, black points are RFID positions.
The blue grid shows a position probability of histogram filter.
In this simulation, x,y are unknown, yaw is known.
The filter integrates speed input and range observations from RFID for localization.
Initial position is not needed.
PROBABILISTIC ROBOTICSMapping
This is a 2D Gaussian grid mapping example.
This is a 2D ray casting grid mapping example.
This is a 2D object clustering with k-means algorithm.
This is an object shape recognition using circle fitting.
The blue circle is the true object shape.
The red crosses are observations from a ranging sensor.
The red circle is the estimated object shape using circle fitting.
Simultaneous Localization and Mapping(SLAM) examples
This is a 2D ICP matching example with singular value decomposition.
It can calculate a rotation matrix and a translation vector between points to points.
Introduction to Mobile Robotics: Iterative Closest Point Algorithm
This is an Extended Kalman Filter based SLAM example.
The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with EKF SLAM.
The green crosses are estimated landmarks.
This is a feature based SLAM example using FastSLAM 1.0.
The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with FastSLAM.
The red points are particles of FastSLAM.
Black points are landmarks, blue crosses are estimated landmark positions by FastSLAM.
SLAM simulations by Tim Bailey
This is a feature based SLAM example using FastSLAM 2.0.
The animation has the same meanings as one of FastSLAM 1.0.
This is a graph based SLAM example.
The blue line is ground truth.
The black line is dead reckoning.
The red line is the estimated trajectory with Graph based SLAM.
The black stars are landmarks for graph edge generation.
A Tutorial on Graph-Based SLAMPath Planning
This is a 2D navigation sample code with Dynamic Window Approach.
The Dynamic Window Approach to Collision Avoidance
This is a 2D grid based shortest path planning with Dijkstra's algorithm.
In the animation, cyan points are searched nodes.
This is a 2D grid based shortest path planning with A star algorithm.
Its heuristic is 2D Euclid distance.
This is a 2D grid based path planning with Potential Field algorithm.
In the animation, the blue heat map shows potential value on each grid.
Robotic Motion Planning:Potential Functions
This is a path optimization sample on model predictive trajectory generator.
This algorithm is used for state lattice planner.
Optimal rough terrain trajectory generation for wheeled mobile robots
This script is a path planning code with state lattice planning.
This code uses the model predictive trajectory generator to solve boundary problem.
State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments
This PRM planner uses Dijkstra method for graph search.
In the animation, blue points are sampled points,
Cyan crosses means searched points with Dijkstra method,
The red line is the final path of PRM.
Probabilistic roadmap - Wikipedia
This Voronoi road-map planner uses Dijkstra method for graph search.
In the animation, blue points are Voronoi points,
Cyan crosses mean searched points with Dijkstra method,
The red line is the final path of Vornoi Road-Map.
Robotic Motion Planning
This is a simple path planning code with Rapidly-Exploring Random Trees (RRT)
Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions.
This is a path planning code with RRT*
Incremental Sampling-based Algorithms for Optimal Motion Planning
Sampling-based Algorithms for Optimal Motion Planning
Path planning for a car robot with RRT and dubins path planner.
Path planning for a car robot with RRT* and dubins path planner.
)
Path planning for a car robot with RRT* and reeds sheep path planner.
This is a path planning code with Informed RRT*.
The cyan ellipse is the heuristic sampling domain of Informed RRT*.
Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic
This is a path planning code with Batch Informed RRT*.
Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
A vehicle model based path planning with closed loop RRT*.
In this code, pure-pursuit algorithm is used for steering control,
PID is used for speed control.
Motion Planning in Complex Environments using Closed-loop Prediction
Real-time Motion Planning with Applications to Autonomous Urban Driving
[1601.06326] Sampling-based Algorithms for Optimal Motion Planning Using Closed-loop Prediction
This is a path planning simulation with LQR-RRT*.
A double integrator motion model is used for LQR local planner.
LQR-RRT*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension Heuristics
MahanFathi/LQR-RRTstar: LQR-RRT* method is used for random motion planning of a simple pendulum in its phase plot
A sample code for cubic path planning.
This code generates a curvature continuous path based on x-y waypoints with cubic spline.
Heading angle of each point can be also calculated analytically.
This is a path planning with B-Spline curse.
If you input waypoints, it generates a smooth path with B-Spline curve.
The final course should be on the first and last waypoints.
B-spline - Wikipedia
This is a path planning with Eta^3 spline.
\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots
A sample code of Bezier path planning.
It is based on 4 control points Beier path.
If you change the offset distance from start and end point,
You can get different Beizer course:
Continuous Curvature Path Generation Based on Bezier Curves for Autonomous Vehicles
Motion planning with quintic polynomials.
It can calculate 2D path, velocity, and acceleration profile based on quintic polynomials.
Local Path Planning And Motion Control For Agv In Positioning
A sample code for Dubins path planning.
Dubins path - Wikipedia
A sample code with Reeds Shepp path planning.
15.3.2 Reeds-Shepp Curves
optimal paths for a car that goes both forwards and backwards
ghliu/pyReedsShepp: Implementation of Reeds Shepp curve.
A sample code using LQR based path planning for double integrator model.
This is optimal trajectory generation in a Frenet Frame.
The cyan line is the target course and black crosses are obstacles.
The red line is predicted path.
Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame
Optimal trajectory generation for dynamic street scenarios in a Frenet Frame
Path tracking simulation with pure pursuit steering control and PID speed control.
The red line is a target course, the green cross means the target point for pure pursuit control, the blue line is the tracking.
A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles
Path tracking simulation with Stanley steering control and PID speed control.
Stanley: The robot that won the DARPA grand challenge
Automatic Steering Methods for Autonomous Automobile Path Tracking
Path tracking simulation with rear wheel feedback steering control and PID speed control.
Path tracking simulation with LQR steering control and PID speed control.
ApolloAuto/apollo: An open autonomous driving platform
Path tracking simulation with LQR speed and steering control.
Towards fully autonomous driving: Systems and algorithms - IEEE Conference Publication
Path tracking simulation with iterative linear model predictive speed and steering control.
notebookLicense
MIT
A small PR like bug fix is welcome.
If your PR is merged multiple times, I will add your account to the author list.
You can support this project financially via Patreon.
You can get e-mail technical supports about the codes if you are being a patron.
Atsushi Sakai is creating Open Source Software | Patreon
PayPal donation is also welcome.
Atsushi Sakai (@Atsushi_twi)
Daniel Ingram
Joe Dinius
Karan Chawla
Antonin RAFFIN
Alexis Paques
量子機器學習-量子機器人
The computing field must have a change from classical to quantum.
計算領域必須從經典變為量子。
https://github.com/krishnakumarsekar/awesome-quantum-machine-learning
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
21.09.2017
Lecture 01 - Introduction to Computer Vision and Visual Odometry
Slides (last update 21.09.2017)
Visual odometry tutorial Part I
Visual odometry tutorial Part II
SLAM survey paper
28.09.2017
Lecture 02 - Image Formation 1: perspective projection and camera models
Slides (last update 27.09.2017)
05.10.2017
Lecture 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
12.10.2017
Lecture 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)
19.10.2017
Lecture 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)
26.10.2017
Lecture 06 - Point Feature Detectors, Part 2
Slides (last update 26.10.2017)
Additional reading on feature detection
02.11.2017
Lecture 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)
09.11.2017
Lecture 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)
16.11.2017
Lecture 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)
23.11.2017
Lecture 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
30.11.2017
Lecture 11 - Optical Flow and Tracking (Lucas-Kanade)
Exercise 07 - Lucas-Kanade tracker
Additional reading on Lucas-Kanade
Exercise 07 (last update 30.11.2017)
Solutions (last update 06.12.2017)
07.12.2017
Lecture 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)
14.12.2017
Lecture 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)
21.12.2017
Lecture 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).
Description
Link(size)
Project Specification
vo_project_statement.pdf (600 kB, last updated 22.11.2017)
FAQ
Frequently 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 datasets
main.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
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:
Power Point slides: AMR_lecture_slides.zip
Videos Part 1: AMR_lecture_videos_1.zip
Videos Part 2: AMR_lecture_videos_2.zip
Videos Part 3: AMR_lecture_videos_3.zip
Videos Part 4: AMR_lecture_videos_4.zip
Videos Part 5: AMR_lecture_videos_5.zip