Probabilistic object recognition and pose estimation by fusing multiple algorithms

Reliable object recognition is a mandatory prerequisite for Service Robots in everyday environments. Typical approaches for object recognition use single algorithms or features. However, none is yet able to classify across all types of objects and the field of object recognition is thus still an open challenge. We propose an approach for object recognition and pose estimation that combines existing algorithms. Probabilistic methods are used to fuse the classification and pose estimation results, considering the error introduced by the measurements, actuators (sensor on manipulator) and algorithms. Since integration is one of the real challenges from the laboratory towards the real world, we demonstrate the approach in two fully integrated scenarios. We run the experiments on two platforms and focus on the distinction of few but similar objects.