Table of Contents

1 Introduction.- Applications of SLAM, Joint Estimation, Posterior Estimation, The Extended Kalman Filter, Structure and Sparsity in SLAM, FastSLAM, Outline.- 2 The SLAM Problem.- Problem Definition, SLAM Posterior, SLAM as a Markov Chain, Extended Kalman Filtering, Scaling SLAM Algorithms, Robust Data Association, Comparison of FastSLAM to Existing Techniques.- 3 FastSLAM 1.0.- Particle Filtering, Factored Posterior Representation, The FastSLAM 1.0 Algorithm, FastSLAM with Unknown Data Association, Summary of the FastSLAM Algorithm, FastSLAM Extensions, Log(N) FastSLAM, Experimental Results, Summary.- 4 FastSLAM 2.0.- Sample Impoverishment, FastSLAM 2.0, FastSLAM 2.0 Convergence, Experimental Results, Grid-based FastSLAM, Summary.- 5 Dynamic Environments.- SLAM With Dynamic Landmarks, Simultaneous Localization and People Tracking, FastSLAP Implementation,Experimental Results, Summary.- 6 Conclusions.- Conclusions, Future Work.- References, Index.