The graph-based formulation of the navigation problem is establishing itself as one of the standard ways to formulate the navigation problem within the sensor fusion community. It enables a convenient way to access information from previous positions which can be used to enhance the estimate of the current position.To restrict working memory usage, map partitioning can be used to store older parts of the map on a hard drive, in the form of submaps. This limits the number of previous positions within the active map. This thesis examines the effect that map partitioning information loss has on the state of the art positioning algorithm iSAM2, both in open routes and when loop closure is achieved. It finds that larger submaps appear to cause a smaller positional error than smaller submaps for open routes. The smaller submaps seem to give smaller positional error than larger submaps when loop closure is achieved. The thesis also examines how the density of landmarks at the partition point affects the positional error, but the obtained result is mixed and no clear conclusions can be made. Finally it reviews some loop closure detection algorithms that can be convenient to pair with the iSAM2 algorithm.