  In appearance-based localization and mapping, loop closure detection is the
process used to determinate if the current observation comes from a previously
visited location or a new one. As the size of the internal map increases, so
does the time required to compare new observations with all stored locations,
eventually limiting online processing. This paper presents an online loop
closure detection approach for large-scale and long-term operation. The
approach is based on a memory management method, which limits the number of
locations used for loop closure detection so that the computation time remains
under real-time constraints. The idea consists of keeping the most recent and
frequently observed locations in a Working Memory (WM) used for loop closure
detection, and transferring the others into a Long-Term Memory (LTM). When a
match is found between the current location and one stored in WM, associated
locations stored in LTM can be updated and remembered for additional loop
closure detections. Results demonstrate the approach's adaptability and
scalability using ten standard data sets from other appearance-based loop
closure approaches, one custom data set using real images taken over a 2 km
loop of our university campus, and one custom data set (7 hours) using virtual
images from the racing video game ``Need for Speed: Most Wanted''.
