Added USAGE file with a short tutorial

Fixed typo in README

README

@@ -16,7 +16,7 @@ Directory structure:
   matlab  MATLAB proxy classes and wrappers
 
 
-Boost Depedencies:
+Boost Dependencies:
 ------------------
 The GTSAM library is based on the 'Boost C++ Libraries' which can be
 found here: http://www.boost.org/. 

USAGE

@@ -0,0 +1,66 @@
+USAGE - Georgia Tech Smoothing and Mapping library
+---------------------------------------------------
+
+What is this file?
+
+	This file explains how to make use of the library for common SLAM tasks, 
+	using a visual SLAM implementation as an example.
+	
+	
+Getting Started
+---------------------------------------------------
+Install:
+	Follow the installation instructions in the README file to build and 
+	install gtsam, as well as running tests to ensure the library is working
+	properly.
+
+Compiling/Linking with gtsam:
+  	The installation creates a binary "libgtsam" at the installation prefix,
+  	and an include folder "gtsam".  These are the only required includes, but 
+  	the library has also been designed to make use of XML serialization through
+  	the Boost.serialization library, which requires the the Boost.serialization
+  	headers and binaries to be linked.  
+
+Examples:
+	To see how the library works, examine the unit tests provided.  
+ 
+
+Overview
+---------------------------------------------------
+The gtsam library has three primary components necessary for the construction
+of factor graph representation and optimization which users will need to 
+adapt to their particular problem.  
+
+FactorGraph:
+	A factor graph contains a set of variables to solve for (i.e., robot poses,
+	landmark poses, etc.) and a set of constraints between these variables, which 
+	make up factors.  
+Config: 
+	A configuration is a single object containing labeled values for all of the 
+	variables.  Currently, all variables are labeled with strings, but the type 
+	or organization of the variables can change
+Factors:
+	A nonlinear factor expresses a constraint between variables, which in the
+	SLAM example, is a measurement such as a visual reading on a landmark or
+	odometry.
+
+
+VSLAM Example
+---------------------------------------------------
+The visual slam example shows a full implementation of a slam system.  The example contains
+derived versions of NonlinearFactor, NonlinearFactorGraph, and a Config, in classes
+VSLAMFactor, VSLAMGraph, and VSLAMConfig, respectively.  
+
+The clearest example of the use of the graph to find a solution is in 
+testVSLAMGraph.  The basic process for using graphs is as follows (and can be seen in 
+the test):
+  - Create a NonlinearFactorGraph object (VSLAMGraph)
+  - Add factors to the graph (note the use of Boost.shared_ptr here) (VSLAMFactor)
+  - Create an initial configuration (VSLAMConfig)
+  - Create an elimination ordering of variables (this must include all variables)
+  - Create and initialize a NonlinearOptimizer object (Note that this is a generic 
+      algorithm that does not need to be derived for a particular problem)
+  - Call optimization functions with the optimizer to optimize the graph
+  - Extract an updated configuration from the optimizer
+ 
+