Long-Term Topological Place Learning

In this work, we consider long-term topological place learning and present an approach that enables the robot to learn in an unsupervised, organized and incremental manner. The knowledge associated with the previously visited places is internally stored in the form of bubble descriptor semantic tree (BDST) using the previously proposed bubble space representation. The BDST is generated and maintained without any external supervision. It organizes the learned knowledge where the terminal nodes are viewed as corresponding to distinct places while its structure encodes their semantic hierarchy. In case the robot is not able to recognize a place with its current BDST, it learns it via updating the BDST incrementally based on the hierarchical single link clustering algorithm SLINK. The proposed approach is evaluated experimentally using combined benchmark datasets from indoor and outdoor settings with recognition rates comparable to those of state-of-the-art approaches while the robot is able to retain efficiently and use the knowledge associated with the learned places.