  Multimodal integration is a key component of allowing robots to perceive the
world. Multimodality comes with multiple challenges that have to be considered,
such as how to integrate and fuse the data. In this paper, we compare different
possibilities of fusing visual, tactile and proprioceptive data. The data is
directly recorded on the NICOL robot in an experimental setup in which the
robot has to classify containers and their content. Due to the different nature
of the containers, the use of the modalities can wildly differ between the
classes. We demonstrate the superiority of multimodal solutions in this use
case and evaluate three fusion strategies that integrate the data at different
time steps. We find that the accuracy of the best fusion strategy is 15% higher
than the best strategy using only one singular sense.
