examples with lago and GN

examples/Pose2SLAMExample_g2o.cpp

@@ -10,8 +10,10 @@
  * -------------------------------------------------------------------------- */
 
 /**
- * @file Pose2SLAMExample.cpp
+ * @file Pose2SLAMExample_g2o.cpp
- * @brief A 2D Pose SLAM example that reads input from g2o and uses robust kernels in optimization
+ * @brief A 2D Pose SLAM example that reads input from g2o, converts it to a factor graph and does the
+ * optimization. Output is written on a file, in g2o format
+ * Syntax for the script is ./Pose2SLAMExample_g2o input.g2o output.g2o
  * @date May 15, 2014
  * @author Luca Carlone
  */
@@ -44,7 +46,7 @@ int main(const int argc, const char *argv[]){
 
   // Add prior on the pose having index (key) = 0
   NonlinearFactorGraph graphWithPrior = graph;
-  noiseModel::Diagonal::shared_ptr priorModel = noiseModel::Diagonal::Variances((Vector(3) << 0.01, 0.01, 0.001));
+  noiseModel::Diagonal::shared_ptr priorModel = noiseModel::Diagonal::Variances((Vector(3) << 1e-6, 1e-6, 1e-8));
   graphWithPrior.add(PriorFactor<Pose2>(0, Pose2(), priorModel));
 
   std::cout << "Optimizing the factor graph" << std::endl;

examples/Pose2SLAMExample_lago.cpp

@@ -0,0 +1,62 @@
+/* ----------------------------------------------------------------------------
+
+ * GTSAM Copyright 2010, Georgia Tech Research Corporation,
+ * Atlanta, Georgia 30332-0415
+ * All Rights Reserved
+ * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
+
+ * See LICENSE for the license information
+
+ * -------------------------------------------------------------------------- */
+
+/**
+ * @file Pose2SLAMExample_lago.cpp
+ * @brief A 2D Pose SLAM example that reads input from g2o, and solve the Pose2 problem
+ * using LAGO (Linear Approximation for Graph Optimization). See class LagoInitializer.h
+ * Output is written on a file, in g2o format
+ * Syntax for the script is ./Pose2SLAMExample_lago input.g2o output.g2o
+ * @date May 15, 2014
+ * @author Luca Carlone
+ */
+
+#include <gtsam/geometry/Pose2.h>
+#include <gtsam/inference/Key.h>
+#include <gtsam/slam/PriorFactor.h>
+#include <gtsam/slam/dataset.h>
+#include <gtsam/nonlinear/LagoInitializer.h>
+#include <gtsam/nonlinear/NonlinearFactorGraph.h>
+#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
+#include <gtsam/nonlinear/Values.h>
+#include <fstream>
+#include <sstream>
+
+using namespace std;
+using namespace gtsam;
+
+
+int main(const int argc, const char *argv[]){
+
+  if (argc < 2)
+    std::cout << "Please specify: 1st argument: input file (in g2o format) and 2nd argument: output file" << std::endl;
+  const string g2oFile = argv[1];
+
+  NonlinearFactorGraph graph;
+  Values initial;
+  readG2o(g2oFile, graph, initial);
+
+  // Add prior on the pose having index (key) = 0
+  NonlinearFactorGraph graphWithPrior = graph;
+  noiseModel::Diagonal::shared_ptr priorModel = noiseModel::Diagonal::Variances((Vector(3) << 1e-6, 1e-6, 1e-8));
+  graphWithPrior.add(PriorFactor<Pose2>(0, Pose2(), priorModel));
+
+  std::cout << "Computing LAGO estimate" << std::endl;
+  Values estimateLago = initializeLago(graphWithPrior);
+  std::cout << "done!" << std::endl;
+
+  const string outputFile = argv[2];
+  std::cout << "Writing results to file: " << outputFile << std::endl;
+  writeG2o(outputFile, graph, estimateLago);
+  std::cout << "done! " << std::endl;
+
+  return 0;
+}