Autonomous Path Following By Andrey Zhdanov and Dan Feldman.

Slides:



Advertisements
Similar presentations
CSE 424 Final Presentation Team Members: Edward Andert Shang Wang Michael Vetrano Thomas Barry Roger Dolan Eric Barber Sponsor: Aviral Shrivastava.
Advertisements

CV in a Nutshell (||) Yi Li inNutshell.htm.
Vision-based Motion Planning for an Autonomous Motorcycle on Ill-Structured Road Reporter :鄒嘉恆 Date : 08/31/09.
Pose Estimation and Segmentation of People in 3D Movies Karteek Alahari, Guillaume Seguin, Josef Sivic, Ivan Laptev Inria, Ecole Normale Superieure ICCV.
Signal and System I The inverse z-transform Any path in the ROC to make the integral converge Example ROC |z|>1/3.
Project RFP’s Brian Bayliss Mathew Dicke Nathan Ferrel.
LING 111 Teaching Demo By Tenghui Zhu Introduction to Edge Detection Image Segmentation.
Vision Based Control Motion Matt Baker Kevin VanDyke.
A Gimp Plugin that uses “GrabCut” to perform image segmentation
Jon Schendt University of Wisconsin-Platteville Image Processing – A Computational Approach.
Error detection and concealment for Multimedia Communications Senior Design Fall 06 and Spring 07.
1 Augmenting Path Algorithm s t G: Flow value = 0 0 flow capacity.
Robotics Simulator Intelligent Systems Lab. What is it ? Software framework - Simulating Robotics Algorithms.
Yiming Zhang SUNY at Buffalo TRAFFIC SIGN RECOGNITION WITH COLOR IMAGE.
1 Autonomously Controlled Vehicles with Collision Avoidance Mike Gregoire Rob Beauchamp Dan Holcomb Tim Brett.
1 Color Segmentation: Color Spaces and Illumination Mohan Sridharan University of Birmingham
1 Augmenting Path Algorithm s t G: Flow value = 0 0 flow capacity.
1 Autonomously Controlled Vehicles with Collision Avoidance Mike Gregoire Rob Beauchamp Dan Holcomb Tim Brett.
Object Recognition Using Distinctive Image Feature From Scale-Invariant Key point D. Lowe, IJCV 2004 Presenting – Anat Kaspi.
SWE 423: Multimedia Systems Project #1: Image Segmentation Using Graph Theory.
Feature extraction Feature extraction involves finding features of the segmented image. Usually performed on a binary image produced from.
Traffic Sign Recognition Jacob Carlson Sean St. Onge Advisor: Dr. Thomas L. Stewart.
Chapter 6 Color Image Processing Chapter 6 Color Image Processing.
Brief overview of ideas In this introductory lecture I will show short explanations of basic image processing methods In next lectures we will go into.
The CarBot Project Group Members: Chikaod Anyikire, Odi Agenmonmen, Robert Booth, Michael Smith, Reavis Somerville ECE 4006 November 29 th 2005.
Tal Mor  Create an automatic system that given an image of a room and a color, will color the room walls  Maintaining the original texture.
PixelLaser: Range scans from image segmentation Nicole Lesperance ’11 Michael Leece ’11 Steve Matsumoto ’12 Max Korbel ’13 Kenny Lei ’15 Zach Dodds ‘62.
Presented by: Kamakhaya Argulewar Guided by: Prof. Shweta V. Jain
IMPLEMENTATION ISSUES REGARDING A 3D ROBOT – BASED LASER SCANNING SYSTEM Theodor Borangiu, Anamaria Dogar, Alexandru Dumitrache University Politehnica.
Parallel Edge Detection Daniel Dobkin Asaf Nitzan.
ROBOT LOCALISATION & MAPPING: NAVIGATION Ken Birbeck.
CEG 4392 : Maze Solving Robot Presented by: Dominic Bergeron George Daoud Bruno Daoust Erick Duschesneau Bruno Daoust Erick Duschesneau Martin Hurtubise.
Recognition using Regions (Demo) Sudheendra V. Outline Generating multiple segmentations –Normalized cuts [Ren & Malik (2003)] Uniform regions –Watershed.
Segmentation support in Slicer Csaba Pinter Laboratory for Percutaneous Surgery, Queen’s University, Canada.
MESA LAB Two papers in icfda14 Guimei Zhang MESA LAB MESA (Mechatronics, Embedded Systems and Automation) LAB School of Engineering, University of California,
Color Segmentation & Introduction to Motion Planning CSE350/ Sep 03.
ROBOT NAVIGATION By: Sitapa Rujikietgumjorn Harika Tandra Neeharika Jarajapu.
DIGITAL IMAGE PROCESSING
 Detecting system  Training system Human Emotions Estimation by Adaboost based on Jinhui Chen, Tetsuya Takiguchi, Yasuo Ariki ( Kobe University ) User's.
Computer Graphics and Image Processing (CIS-601).
Autonomous Soil Investigator. What Is the ASI? Designed to complete the 2013 IEEE student robotics challenge Collects "soil" samples from a simulated.
THE CHALLENGE OF DIGITAL PHOTOGRAPHY Delivering Digital Files.
1 Research Question  Can a vision-based mobile robot  with limited computation and memory,  and rapidly varying camera positions,  operate autonomously.
Yingcai Xiao Chapter 10 Image Processing. Outline Motivation DWA: a real world example Algorithms Code examples.
The Implementation of Markerless Image-based 3D Features Tracking System Lu Zhang Feb. 15, 2005.
(Diploma thesis) Student: Peter Polak Supervisor: Rudolf Jaksa.
Corner Detection & Color Segmentation CSE350/ Sep 03.
Project Overview  Autonomous robot  Simulates behavior of dog fetching  Tracks a thrown object, picks it up, and returns it to thrower  Able to avoid.
Vision and Obstacle Avoidance In Cartesian Space.
Counting windows Project participants (in alphabetical order): Akif Durdu Middle East Technical University, Turkiye Viktor Jónás Budapest Polytechnic,
1 Motivation Problem: Amateur photographers take unappealing pictures (e.g. personal and business use) Help users take better pictures with digital cameras.
Application of Facial Recognition in Biometric Security Kyle Ferris.
Blind Inverse Gamma Correction (Hany Farid, IEEE Trans. Signal Processing, vol. 10 no. 10, October 2001) An article review Merav Kass January 2003.
Project Minotaur Patent Liability Assessment Jon Roose for Team 16.
Laboratory of Image Processing Pier Luigi Mazzeo July 25, 2014.
Learning Roomba Module 5 - Localization. Outline What is Localization? Why is Localization important? Why is Localization hard? Some Approaches Using.
Person Following with a Mobile Robot Using Binocular Feature-Based Tracking Zhichao Chen and Stanley T. Birchfield Dept. of Electrical and Computer Engineering.
ECE472/572 - Lecture 14 Morphological Image Processing 11/17/11.
Implementing the By: Matthew Marsh Supervisors: Prof Shaun Bangay Mrs Adele Lobb segmentation technique as a plugin for the GIMP.
Paper Presentation Saumya Gurbani April 19, 2011 Ascari, L.; Bertocchi, U.; Laschi, C.; Stefanini, C.; Starita, A.; Dario, P.;, "A segmentation algorithm.
What is the next line of the proof?
Unweighted Shortest Path Neil Tang 3/11/2010
Paper Presentation Aryeh Zapinsky
Chapter 10 Image Segmentation.
Viability of vanishing point navigation for mobile devices
CSE 321 – Object Detection & Tracking
Computer Vision Basics
Digital Image Processing Week III
Color Image Processing
Introduction Computer vision is the analysis of digital images
Presentation transcript:

Autonomous Path Following By Andrey Zhdanov and Dan Feldman

Main Objectives The robot should be able to follow the path autonomously The robot should be able to detect the presence of path

Assumptions

Planar floor No obstacles, clutter Homogeneous floor, path

Algorithm Motivation – Geodesic Active Contours Implementation – Graph Cuts

Algorithm Overview Image acquisition Color to Greyscale conversion Edge detection Inverse projective transformation Segmentation Driving instructions

Averaging vs PCA – RGB channels

Averaging vs PCA

Calibration

Algorithm Stages

Algorithm Stages – cont.

Algorithm Stages – contd.

Technical problems Lack of precision of robot control Communication