============================================================================SETUP.============================================================================These Matlab files are a convenient interface for the Java librarycontaining the implementation of thin junction tree filters (TJTF).They make use of the fact that Matlab has an internal Java virtualmachine (JVM).  To learn more about this, see   http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_external/ch_java2.shtmlTo have the TJTF java code loaded into the Matlab JVM, your Matlabclasspath.txt file must include references to the root of the javaslampackage, and also to Jama-1.0.1.jar (both of which are contained inthe bin subdirectory of tjtf-java.zip).============================================================================A SIMPLE EXAMPLE.Once your classpath.txt file is set up and the Matlab files in thisdistribution are in your Matlab path, you should be able to executethis simple example.  Once you've run the example, you can tracethrough the code of SLAM_SIM to see how it all works.============================================================================% First, create a simple two-dimensional SLAM problem.p = slam2dprob('num-landmarks', 100, 'side', 20, 'shape', 'square');% Let's take a look at what we just made.  slam2dplot(p);% The landmarks are shown as black dots.  The robot's path is the blue% line.  The red line is the integrated odometry signal, and the green% line is the integrated control signal.  The cyan dots are the% landmark measurements (relative to the robot's true position).% Now let's build a Kalman filter for this problem.  Start by making% an initial covariance for the robot state, which in this case has five% dimensions.xC = 1e-5 * eye(5); % Now create a filter with the initial covariance and the true% starting state of the robot.f = kalman_slam_filter(p.path(:, 1), xC);% Okay, let's watch the Kalman filter do its thing!slam_sim(p, f);============================================================================FILES.Below is a categorization of the files, along with brief descriptions.Most of the files are very well documented and commented, so it shouldbe easy to figure out what they do and how they fit together.  (Someof the comments and documentation are slightly out of date.)============================================================================Building a SLAM problem:-----------------------slamprob.m			# describes a SLAM problem structure  slam2dprob.m			# generates an example SLAM problem    h2rot.m			# converts angle to rotation matrix    slam2dplot.m		# plots an example SLAM problemBuilding a SLAM filter:----------------------slamfilter.m			# describes a SLAM filter structure  kalman_slam_filter.m		# builds a Kalman SLAM filter   information_slam_filter.m	# builds an information SLAM filter  jt_slam_filter.m		# builds a junction tree SLAM filter    tjt_slam_filter.m		# builds a thin junction tree SLAM filterThings to do with a SLAM filter:-------------------------------slam_sim.m			# visualizes a SLAM filter on a SLAM problem  slam_plot.m			# visualizes the belief state of a filterslam_prof.m			# compares several SLAM filters on a problem  slam_err.m			# computes the estimation error of a filterInternal SLAM code:----------------------lgslam.m			# performs a linear-Gaussian filter update lgslam_da.m			# performs maximum likelihood data association  match.m			# wrapper for the hungarian method  hungarian.m			# weighted bipartite matching code  mahal2conf.m			# used for gating in data association  mabsthresh.m			# used for gating in data associationlgslam_map.m			# extracts map from a linear-Gaussian filterlgslam_plot.m			# plots the belief state of a LG filterjt_slam_plot.m			# plots the beliefs of a junction tree filterPlotting code:-------------conf2mahal.m			# computes confidence ellipse sizeplotcov2.m			# plots a 2D confidence ellipseplotcov3.m			# plots a 3D confidence bubblecheckpsd.m			# checks for positive-definite covarianceMiscellaneous code:------------------process_options.m		# argument/option processing used by all filesralign.m			# Rigid alignment of two 2D data sets