  In this paper, we present a framework for real-time autonomous robot
navigation based on cloud and on-demand databases to address two major issues
of human-like robot interaction and task planning in global dynamic
environment, which is not known a priori. Our framework contributes to make
human-like brain GPS mapping system for robot using spatial information and
performs 3D visual semantic SLAM for independent robot navigation. We
accomplish the feat by separating robot's memory system into Long-Term Memory
(LTM) and Short-Term Memory (STM). We also form robot's behavior and knowledge
system by linking these memories to Autonomous Navigation Module (ANM),
Learning Module (LM), and Behavior Planner Module (BPM). The proposed framework
is assessed through simulation using ROS-based Gazebo-simulated mobile robot,
RGB-D camera (3D sensor) and a laser range finder (2D sensor) in 3D model of
realistic indoor environment. Simulation corroborates the substantial practical
merit of our proposed framework.
