Application of new particle-based solutions to the Simultaneous Localization and Mapping (SLAM) problem

Abstract:     In this thesis, we explore novel solutions to the Simultaneous Localization and Mapping (SLAM) problem based on particle filtering and smoothing methods. In essence, the SLAM problem constitutes of two interdependent tasks: map building and tracking. Three solution methods utilizing different smoothing techniques are explored. The smoothing methods used are fixed lag smoothing (FLS), forward-only forward-filtering backward-smoothing (forward-only FFBSm) and the particle-based, rapid incremental smoother (PaRIS). In conjunction with these smoothing techniques the well-established Expectation-Maximization (EM) algorithm is used to produce maximum-likelihood estimates of the map. The three solution method are then evaluated and compared in a simulated setting.