Although the particle filter can be used as an effective means to solve the SLAM problem, but the algorithm still exist some problems in which the most important issue is the need for a large number of sample size with a good approximation to the system a posteriori probability density.

ekfslam
.......\add_control_noise.m
.......\add_observation_noise.m
.......\augment.m
.......\augment_associate_known.m
.......\compute_steering.m
.......\configfile.m
.......\data_associate.m
.......\data_associate_known.m
.......\ekfslam_sim.m
.......\example_densemap.mat
.......\example_densermap.mat
.......\example_linemap.mat
.......\example_webmap.mat
.......\frontend.fig
.......\frontend.m
.......\get_observations.m
.......\KF_cholesky_update.m
.......\KF_IEKF_update.m
.......\KF_simple_update.m
.......\line_plot_conversion.m
.......\observe_heading.m
.......\observe_model.m
.......\pi_to_pi.m
.......\plot_feature_loci.m
.......\predict.m
.......\readme.txt
.......\sqrtm_2by2.m
.......\transformtoglobal.m
.......\update.m
.......\update_iekf.m
.......\vehicle_model.m

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