A Reliable Localization Architecture for Mobile Surveillance Robots

Abstract

The operation of a mobile robot relies significantly on the robustness of its hardware and software, in which the self-localization system plays a crucial role. However, keeping a mobile robot localized at all times is not trivial, as there are diverse sources of error which may cause mislocalizations, affecting the whole system with unforeseen consequences. In this work, we describe an innovative and reliable localization architecture for mobile robots grounded on adaptive Monte Carlo localization (AMCL), sensor fusion and scan-matching techniques for mobile robots in large indoor areas. To validate the system, we run two 24-hour-long surveillance demos with an in-house developed mobile robot, comparing and analyzing a standard and widely used AMCL localization architecture against the proposed system. While the standard AMCL leads to 85% of operation time with accurate localization, the robot under the proposed approach does not depict a single localization issue during the 24-hour pilot.