Map Matching and Data Association for Large-Scale Two-dimensional Laser Scan-based SLAM

Abstract

Reliable data association techniques for simultaneous localization and mapping (SLAM) are necessary for the generation of large-scale maps in unstructured outdoor environments. Data association techniques are required at two levels: the local level represents the inner loop of the mapping algorithm, and the global level where newly mapped areas are matched to previously mapped areas to detect repeated coverage and close loops. Local map building is achieved using a robust iterative scan matching technique incorporated into an extended Kalman filter where the state consists of the current pose and previous poses sampled periodically and at a fixed lag from the current time. The introduction of states at a fixed time lag significantly reduces the growth of errors in the location estimate and the resultant map. For global matching, we enhance existing histogram cross-correlation techniques, introducing entropy sequences of projection histograms and an exhaustive correlation approach for reliable matching in unstructured environments. This enables loop closure without depending on prior knowledge of map alignment. These data association techniques are incorporated into the Atlas SLAM framework, enabling the generation of accurate two-dimensional laser maps over tens of kilometers in challenging outdoor environments.