Supervised feature type selection for topological mapping in indoor environments

Most of the mapping methods in indoor environments use single type of feature. Typically an expert decides which feature type to be used. In this work, the robot selects suitable feature type based on the current place during mapping. During the training phase, an expert labels places with the suitable feature type. The robot calculates a metric for each feature type and trains a hidden Markov model with the labels provided by the expert. In a new environment, the robot uses the trained hidden Markov model and selects suitable feature type. Hierarchical topological mapping enables usage of different feature types in the map representation. This method increases the adaptability of the mapping system in new environments and enables different feature types to be utilized in the same environment. The devised method is applied to a robot equipped with a laser sensor and a camera in a simulation environment and tested in an indoor environment.