We present an improvement to the DP-SLAM algorithm for simultane- ous localization and mapping (SLAM) that maintains multiple hypothe- ses about densely populated maps (one full map per particle in a par- ticle filter) in time that is linear in all significant algorithm parameters and takes constant (amortized) time per iteration. This means that the asymptotic complexity of the algorithm is no greater than that of a pure localization algorithm using a single map and the same number of parti- cles. We also present a hierarchical extension of DP-SLAM that uses a two level particle filter which models drift in the particle filtering process itself. The hierarchical approach enables recovery from the inevitable drift that results from using a finite number of particles in a particle filter and permits the use of DP-SLAM in more challenging domains, while maintaining linear time asymptotic complexity.

Machine learning methods are often applied to the problem of learning a map from a robot's sensor data, but they are rarely applied to the problem of learning a robot's motion model. The motion model, which can be influenced by robot idiosyncrasies and terrain properties, is a crucial aspect of current algorithms for Simultaneous Localization and Mapping (SLAM). In this paper we concentrate on generating the correct motion model for a robot by applying EM methods in conjunction with a current SLAM algorithm. In contrast to previous calibration approaches, we not only estimate the mean of the motion, but also the interdependencies between motion terms, and the variances in these terms. This can be used to provide a more focused proposal distribution to a particle filter used in a SLAM algorithm, which can reduce the resources needed for localization while decreasing the chance of losing track of the robot's position. We validate this approach by recovering a good motion model despite initialization with a poor one. Further experiments validate the generality of the learned model in similar circumstances.

Probabilistic approaches have proved very successful at addressing the basic problems of robot localization and mapping and they have shown great promise on the combined problem of simultaneous localization and mapping (SLAM). One approach to SLAM assumes relatively sparse, relatively unambiguous landmarks and builds a Kalman filter over landmark positions. Other approaches assume dense sensor data which individually are not very distinctive, such as those available from a laser range finder. In earlier work, we presented an algorithm called DP-SLAM, which provided a very accurate solution to the latter case by efficiently maintaining a joint distribution over robot maps and poses. The approach assumed an extremely accurate laser range finder and a deterministic environment. In this work we demonstrate an improved map representation and laser penetration model, an improvement in the asymptotic efficiency of the algorithm, and empirical results of loop closing on a high resolution map of a very challenging domain.