Towards constant time SLAM using postponement

Many recent approaches to Simultaneous Localisation and Mapping (SLAM) use an Extended Kalman Filter (EKF) to update and maintain a map of vehicle location and multiple feature positions as a sensor moves through a scene. Although it is a highly powerful and well-used tool, it suffers from a well-known complexity problem, that the amount of computation at each recursion step is proportional to the square of the number of features in the map. In this paper we outline the Postponement technique which allows for much greater flexibility about when to use available processing time, while in no way affecting the optimality of the filter. It works by updating a constant-sized data set based on current measurements, which can be used to effect updates on all unobserved parts of the map at a later stage. By expanding the set of updated features as each new feature is observed we show that the full map update can be postponed indefinitely. We also demonstrate how Postponement can be used to improve the performance of sub-optimal algorithms by applying it to a simple constant time method.