1. Vision-Based Place Recognition for
Autonomous Robots
First Seminar - Project Overview
Team Members:
Ahmed Abd-El Fattah Mohammed
Ahmed Saher Maher
Mourad Aly Mourad
Yasser Hassan Ahmed
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2. Prof.Dr Mohamed Roushdy
Dr. Mohamed Abdel Megeed
Dr. Safaa Amin
T.A. Mohamed Fathy
Supervisors
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3. Agenda
Objective What Methodology How
Theoretical Background Improve previous work
Motivation Adaptive Multi-Scale
Classification
Problem Definition
Challenges
System Architecture
Testing Platform
Conventional Pattern
Recognition System Architecture Development Tools
Related Work
Time Plan When
ImageCLEF
Our Progress
Top Related Systems
Next Objective
References
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4. Objective
Where am I ?
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5. Theoretical Background
Where are we in the field of computer science ?
Pattern Image
Recognition Processing
Computer
Computer Vision
Science
Artificial
Intelligence
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6. Motivation
Interested in robot vision.
Has many applications, help in rescue missions.
Co-operation between our university and Bielefeld
University.
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7. Problem Definition
Meeting Room
Vision-Based Place Recognition for Autonomous Robot
What does it mean ?
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8. Problem Definition
SLAM
Simultaneous Localization And Mapping.
Our problem is to focus on localization issues in most
SLAM systems.
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9. Conventional PR System Architecture
Training Phase Testing Phase
Sensing Sensing
Pre-Processing Pre-Processing
Feature Extraction Feature Extraction
Training Classification
Knowledge Decision
Base 9
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10. Related Work
ImageCLEF
ImageCLEF 2010
22-23th September 2010
A yearly contest which focuses on information retrieval
using image processing. It branches to many applications
including robot vision.
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11. Related Work
1st Position
CVG
Olivier Saurer, Friedrich Fraundorfer, and Marc
Pollefeys – “Visual localization using global visual
features and vanishing points” - ETH Zurich,
Switzerland
Pros Cons
Focused on feature extraction
Used very primitive classification
phase developed new feature
methods.
extraction algorithms. 11
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12. Related Work
4th Position
Centro Gustavo Stefanini
W.Lucetti, E. Luchetti – “Combination of Classifiers
for Indoor Room Recognition” - Gustavo Stefanini
Research Center - Padua, 23 September 2010
Pros Cons
Focused on classification phase
Used very primitive feature
developed many new
extraction methods.
combination of classifiers. 12
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13. Agenda
Methodology
Objective What How
Theoretical Background Improve previous work
Motivation Adaptive Multi-Scale
Classification
Problem Definition
Challenges
System Architecture
Testing Platform
Conventional Pattern
Recognition System Architecture Development Tools
Time Plan
Related Work When
ImageCLEF Our Progress
Top Related Systems Next Objective
References
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14. Methodology
1. Improve previous work
Combine the pros of each group.
Try to avoid their mistakes and cons.
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15. Methodology
2. Adaptive Multi-Scale Classification
What is the meaning of an environment?
Env 1 Env 2 Env 3
Kitchen,Bathroom LivingRoom,Office BedRoom,Corridor
White illumination White illumination Yellow illumination
Color White Color Blue Color Brown
How can the system differentiate between
environments?
Differentiation using discriminative features only.
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16. Methodology
2. Adaptive Multi-Scale Classification
Unrecognized
Image PR System
for Env 1
(Kitchen,Bathroom)
Environment PR System
Start Current
Operating Environment for Env 2 Decision
Identifier (Office,Library)
PR System
for Env 3
(Bedrooms)
Simple classification
Full-scale PR systems
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17. Challenges
Objects’ appearance varies
due to
Cluttered background.
Difference in illumination.
Imaging conditions.
Recognition algorithms perform differently with different
environments.
It’s difficult to find a solution that is both resource efficient
and perform with high accuracy, due to the very limited
resources of a mobile robot.
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18. Testing Platforms
1) Bielefeld University’s workbench
2) ImageCLEF’s testing dataset.
3) Build our own data acquisition tool.
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19. Development Tools
C++ Matlab
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20. Agenda
Objective What Methodology How
Theoretical Background Improve previous work
Motivation Adaptive Multi-Scale
Classification
Problem Definition
Challenges
System Architecture
Testing Platform
Conventional Pattern
Recognition System Architecture Development Tools
Related Work Time Plan
When
ImageCLEF Our Progress
Top Related Systems Next Objective
References
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21. Time Plan
2010 2011
Sep Oct Nov Dec Jan Feb Mar April May June July
Feasibility Study
Survey(1)-Project
Overview
Survey(2)-Project
In Depth
Developing Simple
PR System
Iterative System
Development
Deployment
Documentation
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22. Our Progress
Survey 1 Survey 2
(Project Overview) (Project in Depth)
-Problem definition. -Description of each
-Commonly used algorithm mentioned
algorithms in pattern in survey 1
recognition.
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23. Next Objective
Simple Pattern Recognition System
Image Decision
ImageCLEF ( Class 1 Or
Data Set Class 2 )
Simple PR
The system has theSystem to differentiate between 2
ability
classes.
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24. References
“The Robot Vision Track at ImageCLEF 2010”Andrzej Pronobis, Marco
Fornoni, Henrik I. Christensen, and Barbara Caputo.
“Evaluation of Bayes, ICA, PCA and SVM Methods for Classification”,
V.C.Chen. Radar Division, US Naval Research Laboratory.
Olivier Saurer, Friedrich Fraundorfer, and Marc Pollefeys – “Visual localization
using global visual features and vanishing points” - ETH Zurich, Switzerland
W.Lucetti, E. Luchetti – “Combination of Classifiers for Indoor Room
Recognition” - Gustavo Stefanini Research Center - Padua, 23 September
2010
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25. Contacts
Blog: autovpr.wordpress.com
Ahmed Saher Maher
a7med.saher@gmail.com
Ahmed Abd El-Fattah
ahmed.abdelfattah1@live.com
Mourad Aly Mourad
mouraad@windowslive.com
Yasser Hassan Ahmed
yasserhtd@hotmail.com Thanks!
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