The most important issue for intelligent mobile robot development is the ability to navigate autonomously in the environment to complete certain tasks. Thus, the indoor localization problem of a mobile robot has become a key component for real applications. In general, two categories of mobile robot localization technique are identified: one is robot pose tracking, and the other is robot global localization. In pose tracking problems such as the simultaneous localization and mapping (SLAM) process, the robot has to find its new pose using the observed landmarks or features in its knowledge base of its previous observations. In global localization methodology, a robot does not have knowledge of its previous pose. It has to find its new pose directly in the environment, such as by using a global positioning system (GPS). On the other hand, an artificial beacon-based localization technique, such as the received signal strength indicator (RSSI), causes higher pose uncertainty. However, the artificial beacon can provide a good initial reference for robot position mapping. The gyrocompass of a mobile robot is suited for short-term dead-reckoning. Also the RGB-D camera of a mobile robot can record meaningful features or landmarks in 3D space. The purpose of this work is to fuse the advantages of these sensors via strategy control by a particle filter to enhance the estimation accuracy of indoor mobile robot localization.