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

Abstract— In human-robot communication it is often important to relate robot sensor readings to concepts used by humans. We suggest to use a virtual sensor (one or several physical sensors with a dedicated signal processing unit for recognition of real world concepts) and a method with which the virtual sensor can be learned 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 image content in a particular direction as buildings or non-buildings. The virtual sensor is trained on a diverse set of image data, using features extracted from gray level images. The features are based on edge orientation, configurations of these edges, and on gray level clustering. To combine these features, the AdaBoost algorithm is applied. Our experiments with an outdoor mobile robot show that the method is able...

• Type: Journal
• Year: 2007
• Where: RAS
• Authors: Martin Persson, Tom Duckett, Achim J. Lilienthal
• Keywords: Gray Level, RAS 2007, Robot Sensor Readings, Virtual Sensor