Real-Time Clustering for Long-Term Autonomy

In the future robots will have to operate autonomously for long periods of time. To achieve this they need to be able to learn directly from their environment without human supervision. The use of clustering methods is one possibility to tackle this challenge. Here we present extensions to affinity propagation, a clustering algorithm proposed by Frey and Dueck [5], which makes it suitable for real-time and long-term use in robotics applications. The proposed extension, called meta-point affinity propagation, introduces so called meta-points which increases the performance of the clustering and allows for incremental usage. Additionally we propose a method that enables us to obtain probabilistic cluster assignments from any affinity propagation based clustering method. We show experimental results on the quality and speed of meta-point affinity propagation as well as the probabilistic cluster assignments. Furthermore, we demonstrate how meta-point affinity propagation allows us to process data which is far beyond what affinity propagation could handle.