Multi-sensor clustering using Layered Affinity Propagation

Current robotic systems carry many diverse sensors such as laser scanners, cameras and inertial measurement units just to name a few. Typically such data is fused by engineering a feature that weights the different sensors against each other in perception tasks. However, in a long-term autonomy setting the sensor readings may change drastically over time which makes a manual feature design impractical. A method that can automatically combine features of different data sources would be highly desirable for adaptation to different environments. In this paper, we propose a novel clustering method, coined Layered Affinity Propagation, for automatic clustering of observations that only requires the definition of features on individual data sources. How to combine these features to obtain a good clustering solution is left to the algorithm, removing the need to create and tune a complicated feature encompassing all sources. We evaluate the proposed method on data containing two very common sensor modalities, images and range information. In a first experiment we show the capability of the method to perform scene segmentation on Kinect data. A second experiment shows how this novel method handles the task of clustering segmented colour and depth data obtained from a Velodyne and camera in an urban environment.