Has Something Changed Here? Autonomous Difference Detection for

Security Patrol Robots

IROS 2007, San Diego, CA, USA

Henrik Andreasson, Martin Magnusson, Achim Lilienthal

AASS Learning Systems Lab, Örebro University, Sweden

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Find five errors…

- ?

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Find five errors… in real environment (3D)

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

1. Motivation

2. Methods involved

1. Registration / Mapping

2. Localization

3. Difference meassure

3. Experimental results

4. Conclusions

Contents

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Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Motivation

Human security patroller

Use ‘difference detection’ to look for: open doors open windows water leaks blocking of fire equipment etc.

Inspection

Learn about the environment

Static / dynamic objects etc.

Progress measurement

1

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Registration / Mapping

2

No odometry

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Registration / Mapping

3

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Henrik Andreasson, Martin Magnusson and Achim Lilienthal

R, t

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Registration / Mapping

ICP:

Closed form solution (obtain R and t in one step)

SVD approach by Arun et. al

2

ICP – Iterative closest point

No need to search for closest point

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Registration / Mapping

2

Wrong correspondences

Use a ’trimmed’ version: use 70% of the closest pairs

(=iterative method)

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Registration / Mapping 2

3 Laser Scanner Sweeps & 7 Images per Robot Pose

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Registration / Mapping

22 Robot Poses (3 scans + 7 images each)

Incremental Registration

2

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Registration / Mapping 2

Links / constraints - determine by the ‘similarity measure’ using visual features

Optimize all poses at once (21 * 6 params…)

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Registration / Mapping

Global optimization:

2

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Registration / Mapping

No optimization

Optimization

2

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Localization

3

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Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Localization

3

New readings

Old readings

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Localization

3

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Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Difference meassure

3

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Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Difference meassure

3

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Methods – Difference meassure

Adjustable cell size (spatial) :

Volume of covariance ellipsoid

Planes has low volume

Adjustable cell size (color)

Largest direction in the ellipsoid

3

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Experimental results - distance

4

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Experimental results – color distance

4

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Conclusions

Difference detection method

Odometry free registration method

Global optimization

Appearance and registration based localization

Tested in indoor laboratory environment

More evaluation

larger sets, outdoors

Improving accuracy/resolution

Use images “directly”

5

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Questions?

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Additional Registration Results

Henrik Andreasson, Martin Magnusson and Achim Lilienthal

Find five errors…

- ?

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