Enriched Indoor Map Construction Based on Multisensor Fusion Approach for Intelligent Service Robot

Abstract

The objective of this paper is to have an intelligent service robot that not only autonomously estimates the environment structure but also simultaneously detects the commonly recognized symbols/signs in the building. The result is an information-enriched map constructed by the environment geometry from a laser ranger and the indoor indicators from visual image. To implement this enriched map, multisensor fusion techniques, i.e., covariance intersection and covariance union, are tactically utilized for robust pose association and sign estimation. Furthermore, an improved alignment technique is applied to promote the mapping precision in a single simultaneous localization and mapping process with the posterior convenience. Additionally, a 2.5-D environment enriched map has been rapidly constructed with the Mesa SwissRanger. We have successfully experimentally demonstrated the proof of concept and summarized it in the conclusion.