A SLAM system based on Hidden Markov Models

Abstract

We present a graph SLAM system based on Hidden Markov Models (HMM) where the sensor readings are represented with different symbols using a number of clustering techniques; then, the symbols are fused as a single prediction, to improve the accuracy rate, using a Dual HMM. Our system’s versatility allows to work with different types of sensors or fusion of sensors, and to implement, either active or passive, graph SLAM. The Toyota HSR (Human Support Robot) robot was used to generate the data set in both real and simulated competition environments. We tested our system in the kidnapped robot problem by training a representation, improving it online, and, finally, solving the SLAM problem.