An iterative approach for building feature maps in cyclic environments

In this paper we deal with one of the fundamental problems for mobile robots, namely their ability to produce accurate representations of their environment. We propose an office mapping algorithm that iteratively alternates between a Kalman smoother based localization step and a map features recalculation step. Moreover, a hybrid algorithm with global localization capabilities is employed in the first step, enabling correct identification of already mapped areas, and, thus, ensuring map correctness in cyclic environments.