3D Object Detection

Automatic, fast and reliable object detection algorithms are essential for mobile robots used in searching tasks. To perceive objects, we use the 3D laser range and reflectance data. The 3D data is transformed into images by off-screen rendering. To detect objects, a cascade of classifiers, i.e., a linear decision tree, is used. Following the ideas of Viola and Jones, we compose each classifier from several simple classifiers, which in turn contain an edge, line or center surround feature \cite{Viola_2001}. There exists an effective method for the fast computation of these features using an intermediate representation, namely, integral image. For learning of the object classes, a boosting technique, namely, Ada Boost, is used \cite{Viola_2001}. The resulting approach for object classification is reliable and real-time capable and combines recent results in computer vision with the emerging technology of 3D laser scanners. For a detailed discussion of object detection in 3D laser range data, refer to [14]}. Figure chair shows an office chair detected by a cascade of classifiers.