Abstract: The SLAM problem as originally posed is now well understood; one might say “solved” in small local areas. There is, however, much to be done in producing useful maps and representations of truly large workspaces and in obtaining sustained and robust operation within them. This talk will describe work on these issues and will review our work on appearance based and metric navigation and mapping over large scales and in particular how it solves the “loop closing” problem which plagues online infrastructure-free navigation algorithms. We shall also review work on detailed acquisition and semantic labeling of workspaces using both laser and appearance information. In summary the “L” is done but the “M” needs work.