Semi-supervised learning for mobile robot localization using wireless signal strengths

This paper proposes a new semi-supervised machine learning for localization. It improves localization efficiency by reducing efforts needed to calibrate labeled training data by using unlabeled data, where training data come from received signal strengths of a wireless communication link. The main idea is to treat training data as spatio-temporal data. We compare the proposed algorithm with the state-of-art semi-supervised learning methods. The algorithms are evaluated for estimating the unknown location of a smartphone mobile robot. The experimental results show that the developed learning algorithm is the most accurate and robust to the varying amount of training data, without sacrificing the computation speed.