Fuzzy multisensor fusion for autonomous proactive robot perception

Robot perception still lacks reliability in complex natural environments. A commonly used method to improve perception is to incorporate more sensors with different modalities. This leads to increased computational requirements due to the parallel processing of huge amounts of sensor data. Appropriate sensor fusion methods are needed if contradictory information is provided by different sensors. We propose a feature-based technique to fuse multimodal sensor data using fuzzy rules. Probabilistic methods are avoided by applying fuzzyfication at the feature level. We propose a higher information gain of the available sensors by utilizing robot actions to focus sensors on objects of interest. Therefore sensor readings, algorithms and robot actions are combined into feature detectors. A goal-directed activation of these feature detectors renders parallel processing of all sensor data unnecessary.