The ability for a robotic system to fully and autonomously interact with its environment is key to the future of applications such as commercial package delivery services, elderly robotic assistants, agricultural monitoring systems, natural disaster search and rescue robots, civil construction monitoring systems, robotic satellite servicing, and many more. An architecture that is conducive to Simultaneous Localization And Mapping (SLAM), path planning, and mission planning is a critical element of a system to be robust enough to handle such applications with true autonomy. In this paper we present an architecture that lends itself to such cohesive operation of all the aforementioned goals through the implementation of a common core database to represent the environment. We present the overall architecture followed by a description of the components of the architecture and how they interact, including: a demonstration of image processing techniques using geographic information science (GIS) analytical methods and ellipsoid feature models, an explanation of database management tools using k-vector, an outline of the SLAM approach, and a description of the path planning algorithm employed.