DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks

Abstract

We present a novel, laser range finder based algorithm for simultaneous localization and mapping (SLAM) for mobile robots. SLAM addresses the problem of constructing an accurate map in real time despite imperfect information about the robot's trajectory through the environment. Unlike other approaches that assume predetermined landmarks (and must deal with a resulting dataassociation problem) our algorithm is purely laser based. Our algorithm uses a particle filter to represent both robot poses and possible map configurations. By using a new map representation, which we call distributed particle (DP) mapping, we are able to maintain and update hundreds of candidate maps and robot poses efficiently. The worst-case complexity of our algorithm per laser sweep is log-quadratic in the number of particles we maintain and linear in the area swept out by the laser. However, in practice our run time is usually much less than that. Our technique contains essentially no assumptions about the environment yet it is accurate enough to close loops of 60m in length with crisp, perpendicular edges on corridors and minimal or no misalignment errors.