Virtual sensors for human concepts-Building detection by an outdoor mobile robot

Authors:
Martin Persson;Tom Duckett;Achim Lilienthal
Affiliations:
Center for Applied Autonomous Sensor Systems, Department of Technology, Örebro University, Örebro, Sweden;Department of Computing and Informatics, University of Lincoln, Lincoln, UK;Center for Applied Autonomous Sensor Systems, Department of Technology, Örebro University, Örebro, Sweden
Venue:
Robotics and Autonomous Systems
Year:
2007

Citing 4
Cited 2

A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A Brief Introduction to Boosting

IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Consistent Line Clusters for Building Recognition in CBIR

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

Robotics and Autonomous Systems
Towards an architecture for semiautonomous robot telecontrol systems

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In human-robot communication it is often important to relate robot sensor readings to concepts used by humans. We suggest the use of a virtual sensor (one or several physical sensors with a dedicated signal processing unit for the recognition of real world concepts) and a method with which the virtual sensor can learn from a set of generic features. The virtual sensor robustly establishes the link between sensor data and a particular human concept. In this work, we present a virtual sensor for building detection that uses vision and machine learning to classify the image content in a particular direction as representing buildings or non-buildings. The virtual sensor is trained on a diverse set of image data, using features extracted from grey level images. The features are based on edge orientation, the configurations of these edges, and on grey level clustering. To combine these features, the AdaBoost algorithm is applied. Our experiments with an outdoor mobile robot show that the method is able to separate buildings from nature with a high classification rate, and to extrapolate well to images collected under different conditions. Finally, the virtual sensor is applied on the mobile robot, combining its classifications of sub-images from a panoramic view with spatial information (in the form of location and orientation of the robot) in order to communicate the likely locations of buildings to a remote human operator.