Fixed some top-level files

README.md

@@ -40,10 +40,12 @@ Optional prerequisites - used automatically if findable by CMake:
 Additional Information
 ----------------------
 
-Read about important [`GTSAM-Concepts`] here.
+Read about important [`GTSAM-Concepts`](GTSAM-Concepts.md) here.
 
-See the [`INSTALL`] file for more detailed installation instructions.
+See the [`INSTALL`](INSTALL) file for more detailed installation instructions.
 
-GTSAM is open source under the BSD license, see the [`LICENSE`](https://bitbucket.org/gtborg/gtsam/src/develop/LICENSE) and [`LICENSE.BSD`](https://bitbucket.org/gtborg/gtsam/src/develop/LICENSE.BSD) files.
+GTSAM is open source under the BSD license, see the [`LICENSE`](LICENSE) and [`LICENSE.BSD`](LICENSE.BSD) files.
 
-Please see the [`examples/`](examples) directory and the [`USAGE`] file for examples on how to use GTSAM.
+Please see the [`examples/`](examples) directory and the [`USAGE`](USAGE.md) file for examples on how to use GTSAM.
+
+GTSAM was developed in the lab of [Frank Dellaert](http://www.cc.gatech.edu/~dellaert) at the [Georgia Institute of Technology](http://www.gatech.edu), with the help of many contributors over the years, see [THANKS](THANKS).

THANKS

@@ -1,20 +1,39 @@
 GTSAM was made possible by the efforts of many collaborators at Georgia Tech
 
+Sungtae An
 Doru Balcan
 Chris Beall
+Luca Carlone
 Alex Cunningham
+Jing Dong
 Alireza Fathi
 Eohan George
+Alex Hagiopol
 Viorela Ila
 Yong-Dian Jian
 Michael Kaess
+Zhaoyang Lv
+Andrew Melim
 Kai Ni
 Carlos Nieto
-Duy-Nguyen
+Duy-Nguyen Ta
 Manohar Paluri
 Christian Potthast
 Richard Roberts
 Grant Schindler
+Natesh Srinivasan
+Thomas Schneider
+Alex Trevor
+
+at ETH, Zurich
+
+Paul Furgale
+Mike Bosse
+Hannes Sommer
+
+at Uni Zurich:
+
+Christian Forster
 
 Many thanks for your hard work!!!!
 Frank Dellaert

USAGE.md

@@ -1,6 +1,5 @@
 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, 
@@ -34,18 +33,12 @@ 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:
+* FactorGraph:
-	A factor graph contains a set of variables to solve for (i.e., robot poses,
+	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.  
-	landmark poses, etc.) and a set of constraints between these variables, which 
+* Values: 
-	make up factors.  
+	Values 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
-Values: 
+* Factors:
-	Values is a single object containing labeled values for all of the 
+	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.
-	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.
 
 The library is organized according to the following directory structure:
 
@@ -59,23 +52,3 @@ The library is organized according to the following directory structure:
 
 
 
-VSLAM Example
----------------------------------------------------
-The visual slam example shows a full implementation of a slam system.  The example contains
-derived versions of NonlinearFactor, NonlinearFactorGraph, in classes visualSLAM::ProjectionFactor, 
-visualSLAM::Graph, respectively. The values for the system are stored in the generic 
-Values structure. For definitions and interface, see gtsam/slam/visualSLAM.h. 
-
-The clearest example of the use of the graph to find a solution is in 
-testVSLAM.  The basic process for using graphs is as follows (and can be seen in 
-the test):
-  - Create a NonlinearFactorGraph object (visualSLAM::Graph)
-  - Add factors to the graph (note the use of Boost.shared_ptr here) (visualSLAM::ProjectionFactor)
-  - Create an initial configuration (Values)
-  - 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 values from the optimizer
- 
-