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

Persson, Martin, Duckett, Tom and Lilienthal, Achim (2007) Virtual sensors for human concepts—Building detection by an outdoor mobile robot. Robotics and Autonomous Systems, 55 (5). pp. 383-390. ISSN 0921-8890

Persson_etal_2007-RAS-Virtual_Sensors_for_Human_Concepts_Building_Detection_by_an_Outdoor_Mobile_Robot.pdf - Whole Document

Item Type:Article
Item Status:Live Archive


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.

Keywords:Human–robot communication, Human concepts, Virtual sensor, Automatic building detection, AdaBoost
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:28022
Deposited On:28 Jul 2017 08:46

Repository Staff Only: item control page