Added SmartStereoProjectionFactor example

gtsam/slam/SmartStereoProjectionFactor.h

@@ -296,7 +296,7 @@ public:
           }
           i += 1;
         }
-        std::cout << "totalReprojError error: " << totalReprojError << std::endl;
+        //std::cout << "totalReprojError error: " << totalReprojError << std::endl;
         // we discard smart factors that have large reprojection error
         if(dynamicOutlierRejectionThreshold_ > 0 &&
             totalReprojError/m > dynamicOutlierRejectionThreshold_)

gtsam_unstable/examples/SmartStereoProjectionFactorExample.cpp

@@ -0,0 +1,125 @@
+/* ----------------------------------------------------------------------------
+
+* 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 SmartProjectionFactorExample.cpp
+* @brief A stereo visual odometry example
+* @date May 30, 2014
+* @author Stephen Camp
+* @author Chris Beall
+*/
+
+
+/**
+ * A smart projection factor example based on stereo data, throwing away the
+ * measurement from the right camera
+ *  -robot starts at origin
+ *  -moves forward, taking periodic stereo measurements
+ *  -makes monocular observations of many landmarks
+ */
+
+#include <gtsam/geometry/Pose3.h>
+#include <gtsam/geometry/Cal3_S2Stereo.h>
+#include <gtsam/nonlinear/Values.h>
+#include <gtsam/nonlinear/NonlinearEquality.h>
+#include <gtsam/nonlinear/NonlinearFactorGraph.h>
+#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
+#include <gtsam/inference/Symbol.h>
+#include <gtsam/slam/dataset.h>
+
+#include <gtsam/slam/SmartStereoProjectionPoseFactor.h>
+
+#include <string>
+#include <fstream>
+#include <iostream>
+
+using namespace std;
+using namespace gtsam;
+
+int main(int argc, char** argv){
+
+  typedef SmartStereoProjectionPoseFactor<Pose3, Point3, Cal3_S2Stereo> SmartFactor;
+
+  Values initial_estimate;
+  NonlinearFactorGraph graph;
+  const noiseModel::Isotropic::shared_ptr model = noiseModel::Isotropic::Sigma(2,1);
+
+  string calibration_loc = findExampleDataFile("VO_calibration.txt");
+  string pose_loc = findExampleDataFile("VO_camera_poses_large.txt");
+  string factor_loc = findExampleDataFile("VO_stereo_factors_large.txt");
+  
+  //read camera calibration info from file
+  // focal lengths fx, fy, skew s, principal point u0, v0, baseline b
+  cout << "Reading calibration info" << endl;
+  ifstream calibration_file(calibration_loc.c_str());
+
+  double fx, fy, s, u0, v0, b;
+  calibration_file >> fx >> fy >> s >> u0 >> v0 >> b;
+  const Cal3_S2Stereo::shared_ptr K(new Cal3_S2Stereo(fx, fy, s, u0, v0,b));
+
+  cout << "Reading camera poses" << endl;
+  ifstream pose_file(pose_loc.c_str());
+
+  int pose_id;
+  MatrixRowMajor m(4,4);
+  //read camera pose parameters and use to make initial estimates of camera poses
+  while (pose_file >> pose_id) {
+    for (int i = 0; i < 16; i++) {
+      pose_file >> m.data()[i];
+    }
+    initial_estimate.insert(Symbol('x', pose_id), Pose3(m));
+  }
+  
+  // camera and landmark keys
+  size_t x, l;
+
+  // pixel coordinates uL, uR, v (same for left/right images due to rectification)
+  // landmark coordinates X, Y, Z in camera frame, resulting from triangulation
+  double uL, uR, v, X, Y, Z;
+  ifstream factor_file(factor_loc.c_str());
+  cout << "Reading stereo factors" << endl;
+
+  //read stereo measurements and construct smart factors
+
+  SmartFactor::shared_ptr factor(new SmartFactor());
+  size_t current_l = 3;   // hardcoded landmark ID from first measurement
+
+  while (factor_file >> x >> l >> uL >> uR >> v >> X >> Y >> Z) {
+
+    if(current_l != l) {
+      graph.push_back(factor);
+      factor = SmartFactor::shared_ptr(new SmartFactor());
+      current_l = l;
+    }
+    factor->add(StereoPoint2(uL,uR,v), Symbol('x',x), model, K);
+  }
+
+  Pose3 first_pose = initial_estimate.at<Pose3>(Symbol('x',1));
+  //constrain the first pose such that it cannot change from its original value during optimization
+  // NOTE: NonlinearEquality forces the optimizer to use QR rather than Cholesky
+  // QR is much slower than Cholesky, but numerically more stable
+  graph.push_back(NonlinearEquality<Pose3>(Symbol('x',1),first_pose));
+
+  LevenbergMarquardtParams params;
+  params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
+  params.verbosity = NonlinearOptimizerParams::ERROR;
+
+  cout << "Optimizing" << endl;
+  //create Levenberg-Marquardt optimizer to optimize the factor graph
+  LevenbergMarquardtOptimizer optimizer = LevenbergMarquardtOptimizer(graph, initial_estimate, params);
+  Values result = optimizer.optimize();
+
+  cout << "Final result sample:" << endl;
+  Values pose_values = result.filter<Pose3>();
+  pose_values.print("Final camera poses:\n");
+
+  return 0;
+}