Multi-Stage Localization Given Topological Map for Autonomous Robots Mohammed Abdel-Megeed Salem Faculty of Computer and Information Science, Ain Shams University in Cairo [email protected]

Outline • • • •

Motivation Introduction Robot Localization and Place Recognition Multistage Vision-based Localization – Global Features Extraction and Classification – Local Features Extraction and Classification

• Results • Conclusion

Motivation • Vision-based place recognition for autonomous robots to navigate in a human-inhabited environment, given a topological map.

Mohammed A-Megeed Salem

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Multistage Vision-based Localization Sensing

Image Stream Global Features Extraction and Classification

Ambiguous Classification?

Mohammed A-Megeed Salem

No

Place Recognition

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Multistage Vision-based Localization Sensing

Image Stream Global Features Extraction and Classification

Ambiguous Classification?

No

Place Recognition

Yes Local Features Extraction and Classification

Mohammed A-Megeed Salem

Place Recognition

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Multistage Vision-based Localization

Mohammed A-Megeed Salem

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Global Features Extraction • Global features are recommended as long as the overall composition of the image is desired to be represented, rather than the foreground object. • Typical examples are color histogram and vectors of principle component.

Mohammed A-Megeed Salem

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Global Features Extraction • The global feature vector consists of statistical, spectral and shape features. – The image is divided into 9 disjoint regions – The color model is transformed to the HSV – Mean, and standard deviation of each channel for each block were computed – Statistical Features: 54 for each frame

Mohammed A-Megeed Salem

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Global Features Extraction • The global feature vector consists of statistical, spectral and shape features. – The image is divided into 9 disjoint regions – The 2D Fourier Transform is computed – The highest 10 frequency magnitudes and the corresponding phases are recorded – Spectral Features: 360 for each frame

Mohammed A-Megeed Salem

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Global Features Extraction • The global feature vector consists of statistical, spectral and shape features. – The Hough Line Transform is used – The range of 360 degrees is divided into 6 bins. – The average length of lines in the bin and – The ratio of the number of lines in the bin and the total number of lines. – Shape Features: 12 for each frame. Mohammed A-Megeed Salem

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Local Features Extraction • Scale Invariant Feature Transform (SIFT) – Invariant to rotation, viewpoint change and scaling. – Robust against changes in illumination, and image noise.

• Good results were obtained with 2x2 array of histograms with 8 orientation bins. • Local Features: 2x2x8 = 32 elements for each interest point. Mohammed A-Megeed Salem

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Results • Datasets of the international competition ImageCLEF • two subsets represent different floors in an indoor office environment. • First subset contains 4782 images • Second subset contains 4138 images

Mohammed A-Megeed Salem

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Results

Mohammed A-Megeed Salem

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Results

Mohammed A-Megeed Salem

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Results

Mohammed A-Megeed Salem

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Conclusion • We have proposed a novel approach of localization for autonomous robots using a multistage feature extraction and classification. • The first classification stage is based mainly on global features. • In certain cases the system adds a stage of classification based on local features. Mohammed A-Megeed Salem

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Conclusion • Ambiguous detection allows the system to be robust under different and imaging conditions and providing low misclassification rate. • The system is tested using ImageCLEF dataset, and data set for in house environment.

Mohammed A-Megeed Salem

ICCES 2012 Multi-Stage Localization Given Topological Map for Autonomous Robots

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Thanks for your attention. Multi-Stage Localization Given Topological Map for Autonomous Robots

The International Conference on Computer Engineering and Systems November 27-29, November, 2012, Cairo, Egypt Dr. Mohammed Abdel-Megeed M. Salem Faculty of Computer and Information Sciences, Ain Shams University, Abbassia, Cairo, Egypt Tel.: +2 011 1727 1050 Email: [email protected]

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