Robot Vision SS 2013 Matthias Rüther 1 710.088 ROBOT VISION 2VO 1KU Matthias Rüther, Christian Reinbacher.

Slides:

Advertisements

Similar presentations

Kawada Industries Inc. has introduced the HRP-2P for Robodex 2002

Advertisements

Robot Vision SS 2005 Matthias Rüther ROBOT VISION („Messen aus Bildern“) 2VO 1KU Matthias Rüther Kawada Industries Inc.DLR.

System Integration and Experimental Results Intelligent Robotics Research Centre (IRRC) Department of Electrical and Computer Systems Engineering Monash.

The GraphSLAM Algorithm Daniel Holman CS 5391: AI Robotics March 12, 2014.

Using Perception for mobile robot. 2D ranging for mobile robot.

CPSC 425: Computer Vision (Jan-April 2007) David Lowe Prerequisites: 4 th year ability in CPSC Math 200 (Calculus III) Math 221 (Matrix Algebra: linear.

Probabilistic Robotics: Kalman Filters

Vision for Robotics ir. Roel Pieters & dr. Dragan Kostić Section Dynamics and Control Dept. Mechanical Engineering {r.s.pieters, January.

Advanced Computer Vision Introduction Goal and objectives To introduce the fundamental problems of computer vision. To introduce the main concepts and.

Stanford CS223B Computer Vision, Winter 2005 Lecture 11: Structure From Motion 2 Sebastian Thrun, Stanford Rick Szeliski, Microsoft Hendrik Dahlkamp and.

Part 3 of 3: Beliefs in Probabilistic Robotics. References and Sources of Figures Part 1: Stuart Russell and Peter Norvig, Artificial Intelligence, 2.

Embedded Visual Control for Robotics 5HC99 ir. Roel Pieters & dr. Dragan Kostić Section Dynamics and Control Dept. Mechanical Engineering {r.s.pieters,

SLAM: Simultaneous Localization and Mapping: Part I Chang Young Kim These slides are based on: Probabilistic Robotics, S. Thrun, W. Burgard, D. Fox, MIT.

Multi-view stereo Many slides adapted from S. Seitz.

Stanford CS223B Computer Vision, Winter 2006 Lecture 12 Filters / Motion Tracking 2 Professor Sebastian Thrun CAs: Dan Maynes-Aminzade, Mitul Saha, Greg.

Part 2 of 3: Bayesian Network and Dynamic Bayesian Network.

Stanford CS223B Computer Vision, Winter 2007 Lecture 12 Tracking Motion Professors Sebastian Thrun and Jana Košecká CAs: Vaibhav Vaish and David Stavens.

Introduction to Computer Vision CS223B, Winter 2005.

Computer Vision (CSE P576) Staff Prof: Steve Seitz TA: Jiun-Hung Chen Web Page

3-D Computer Vision Using Structured Light Prepared by Burak Borhan.

1 Three-view geometry 3-view constraint along F Minimal algebraic sol The content described in these slides is not required in the final exam!

© sebastian thrun, CMU, CS226 Statistical Techniques In Robotics Sebastian Thrun (Instructor) and Josh Bao (TA)

Multiple View Geometry : Computational Photography Alexei Efros, CMU, Fall 2006 © Martin Quinn …with a lot of slides stolen from Steve Seitz and.

Computer Vision (CSE/EE 576) Staff Prof: Steve Seitz TA: Aseem Agarwala Web Page

Project 4 Results Representation – SIFT and HoG are popular and successful. Data – Hugely varying results from hard mining. Learning – Non-linear classifier.

Review: Binocular stereo If necessary, rectify the two stereo images to transform epipolar lines into scanlines For each pixel x in the first image Find.

Vision Guided Robotics

Slide 1 ROBOT VISION  2000 Jaskaran Singh ROBOT VISION.

ROBOT MAPPING AND EKF SLAM

Multiple View Geometry in Computer Vision Slides modified from Marc Pollefeys’ online course materials Lecturer: Prof. Dezhen Song.

1 Intelligent Robotics Research Centre (IRRC) Department of Electrical and Computer Systems Engineering Monash University, Australia Visual Perception.

Computational Photography and Videography Christian Theobalt and Ivo Ihrke Winter term 09/10.

Robot Vision SS 2007 Matthias Rüther ROBOT VISION 2VO 1KU Matthias Rüther.

SLAM (Simultaneously Localization and Mapping)

Introduction to Robot Vision

Real-Time High Resolution Photogrammetry John Morris, Georgy Gimel’farb and Patrice Delmas CITR, Tamaki Campus, University of Auckland.

3D Surface Reconstruction from 2D Images (Survey)

오 세 영, 이 진 수 전자전기공학과, 뇌연구센터 포항공과대학교

Peter Sturm INRIA Grenoble – Rhône-Alpes (Institut National de Recherche en Informatique et Automatique) An overview of multi-view stereo and other topics.

KinectFusion : Real-Time Dense Surface Mapping and Tracking IEEE International Symposium on Mixed and Augmented Reality 2011 Science and Technology Proceedings.

Visual Perception PhD Program in Information Technologies Description: Obtention of 3D Information. Study of the problem of triangulation, camera calibration.

(Wed) Young Ki Baik Computer Vision Lab.

Probabilistic Robotics The importance of Mapping & Localization

Probabilistic Robotics: Monte Carlo Localization

Mapping and Localization with RFID Technology Matthai Philipose, Kenneth P Fishkin, Dieter Fox, Dirk Hahnel, Wolfram Burgard Presenter: Aniket Shah.

ECGR4161/5196 – July 26, 2011 Read Chapter 5 Exam 2 contents: Labs 0, 1, 2, 3, 4, 6 Homework 1, 2, 3, 4, 5 Book Chapters 1, 2, 3, 4, 5 All class notes.

G52IVG, School of Computer Science, University of Nottingham 1 Administrivia Timetable Lectures, Friday 14:00 – 16:00 Labs, Friday 17:00 -18:00 Assessment.

Stereo Object Detection and Tracking Using Clustering and Bayesian Filtering Texas Tech University 2011 NSF Research Experiences for Undergraduates Site.

Conceptual and Experimental Vision Introduction R.Bajcsy, S.Sastry and A.Yang Fall 2006.

PCB Soldering Inspection. Structured Highlight approach Structured Highlight method is applied to illuminating and imaging specular surfaces which yields.

Lec 22: Stereo CS4670 / 5670: Computer Vision Kavita Bala.

Peter Henry1, Michael Krainin1, Evan Herbst1,

Robot Mapping Short Introduction to Particle Filters and Monte Carlo Localization.

Tutorial Visual Perception Towards Computer Vision

Robot Vision SS 2007 Matthias Rüther 1 ROBOT VISION Lesson 9: Robots & Vision Matthias Rüther.

Computer Vision, CS766 Staff Instructor: Li Zhang TA: Yu-Chi Lai

COMP 417 – Jan 12 th, 2006 Guest Lecturer: David Meger Topic: Camera Networks for Robot Localization.

Computer Vision Group Prof. Daniel Cremers Autonomous Navigation for Flying Robots Lecture 1.1: Welcome Jürgen Sturm Technische Universität München.

ROBOT VISION Lesson 4: Camera Models and Calibration Matthias Rüther Slides partial courtesy of Marc Pollefeys Department of Computer Science University.

Vision-based SLAM Enhanced by Particle Swarm Optimization on the Euclidean Group Vision seminar : Dec Young Ki BAIK Computer Vision Lab.

HCI/ComS 575X: Computational Perception Instructor: Alexander Stoytchev

Robot Vision SS 2009 Matthias Rüther ROBOT VISION 2VO 1KU Matthias Rüther.

Robot Vision SS 2011 Matthias Rüther / Matthias Straka 1 ROBOT VISION Lesson 7: Volumetric Object Reconstruction Matthias Straka.

UAV for Indoors Inspection and Measurement

제 5 장 스테레오.

CS4670 / 5670: Computer Vision Kavita Bala Lec 27: Stereo.

Multiple View Geometry for Robotics

Computer Vision (CSE 490CV, EE400B)

A Short Introduction to the Bayes Filter and Related Models

Wrap-up Computer Vision Spring 2019, Lecture 26

Presentation transcript:

Robot Vision SS 2013 Matthias Rüther ROBOT VISION 2VO 1KU Matthias Rüther, Christian Reinbacher

Robot Vision SS 2013 Matthias Rüther 2 Administrative VO: 12x Tuesday 14:30-16:00 HS i11 News: tu-graz.lv.robot_vision_ue Exam: Written ( ) in last VO Oral Exam if Requested KU: 33 Students registered  10 Groups of 3 students Target Effort: ~1week per student

Robot Vision SS 2013 Matthias Rüther 3 Robots that „see“ (or want to see)

Robot Vision SS 2013 Matthias Rüther 4 What does „seeing“ mean?  Acquire image(s) –Photon Sensor, Optics, Illumination  Transfer to Processor –Reliably, fast, real-time, long-distance, low noise  2D Image Processing –Feature Detection, Image Segmentation, Image Understanding,...  Reconstruct 3D information –Geometry, Multi-View Geometry, Camera Geometry, Shape from X  Feedback and Manipulation –Robot Control, Kinematic Control, Robot Navigation, Visual Servoing

Robot Vision SS 2013 Matthias Rüther 5 Topics  Multiple View Geometry: –How to algebraically represent points/lines/planes –How to define mappings on these –How to estimate these  Linear Algebra (vectors, matrices, matrix decompositions)  Hardware and Sensors –Cameras, light sources, optics, data transfer, processors  Depth Recovery –How to estimate/measure 3D information from images  RGBD Cameras, Lasers, Projector-Camera Systems, Multi-Cam Systems  Robotics –How to model static/mobile robots  Robot Vision –Visual Odometry, VSLAM, Robotic Metrology, Visual Servoing

Robot Vision SS 2013 Matthias Rüther 6 Literature Hartley R., Zissermann A., Multiple View Geometry in Computer Vision, Cambridge, Second Edition. Sebastian Thrun, Wolfram Burgard and Dieter Fox: Probabilistic Robotics Lecture Slides Web resources

Robot Vision SS 2013 Matthias Rüther 7 Statistics World Robotics 2010, Executive Summary (

Robot Vision SS 2013 Matthias Rüther 8 Statistics World Robotics 2010, Executive Summary (

Robot Vision SS 2013 Matthias Rüther 9 Statistics World Robotics 2010, Executive Summary (

Robot Vision SS 2013 Matthias Rüther 10 Vision Sensors  Passive (Multi-) Camera Systems [Yasutaka Furukawa and Jean Ponce, Accurate, Dense, and Robust Multi-View Stereopsis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009.]  Active Vision Sensors (Emitter/Receiver)

Robot Vision SS 2013 Matthias Rüther 11 Applications Commercial: Body Scanning

Robot Vision SS 2013 Matthias Rüther 12 Applications Microscopy: Fracture Analysis

Robot Vision SS 2013 Matthias Rüther 13 Applications  Structured Light: Part Analysis

Robot Vision SS 2013 Matthias Rüther 14 Applications  Visual SLAM

Robot Vision SS 2013 Matthias Rüther 15 Applications  Robotic Welding

Robot Vision SS 2013 Matthias Rüther 16 Applications  Catadioptric Pose Estimation

Robot Vision SS 2013 Matthias Rüther 17 Applications  Stereoscopic Field Application