fixed stop - go functionality via bagcontrol

include/msckf_vio/feature.hpp

@@ -15,7 +15,7 @@
 #include <Eigen/Dense>
 #include <Eigen/Geometry>
 #include <Eigen/StdVector>
-
+#include <math.h>
 #include <visualization_msgs/Marker.h>
 #include <visualization_msgs/MarkerArray.h>
 #include <geometry_msgs/Point.h>
@@ -70,6 +70,11 @@ struct Feature {
     position(Eigen::Vector3d::Zero()),
     is_initialized(false), is_anchored(false) {}
 
+
+void Rhocost(const Eigen::Isometry3d& T_c0_ci,
+    const double x, const Eigen::Vector2d& z1, const Eigen::Vector2d& z2,
+    double& e) const;
+
   /*
    * @brief cost Compute the cost of the camera observations
    * @param T_c0_c1 A rigid body transformation takes
@@ -82,6 +87,13 @@ struct Feature {
       const Eigen::Vector3d& x, const Eigen::Vector2d& z,
       double& e) const;
 
+  bool initializeRho(const CamStateServer& cam_states);
+
+  inline void RhoJacobian(const Eigen::Isometry3d& T_c0_ci,
+    const double x, const Eigen::Vector2d& z1, const Eigen::Vector2d& z2,
+    Eigen::Matrix<double, 2, 1>& J, Eigen::Vector2d& r,
+    double& w) const; 
+
   /*
    * @brief jacobian Compute the Jacobian of the camera observation
    * @param T_c0_c1 A rigid body transformation takes
@@ -97,6 +109,10 @@ struct Feature {
       Eigen::Matrix<double, 2, 3>& J, Eigen::Vector2d& r,
       double& w) const;
 
+  inline double generateInitialDepth(
+    const Eigen::Isometry3d& T_c1_c2, const Eigen::Vector2d& z1,
+    const Eigen::Vector2d& z2) const;
+
   /*
    * @brief generateInitialGuess Compute the initial guess of
    *    the feature's 3d position using only two views.
@@ -148,6 +164,14 @@ struct Feature {
   inline bool initializePosition(
       const CamStateServer& cam_states);
 
+  cv::Point2f pixelDistanceAt(
+                  const CAMState& cam_state,
+                  const StateIDType& cam_state_id,
+                  const CameraCalibration& cam,
+                  Eigen::Vector3d& in_p) const;
+
+
+
 /*
 *  @brief project PositionToCamera Takes a 3d position in a world frame
 *         and projects it into the passed camera frame using pinhole projection
@@ -160,6 +184,11 @@ struct Feature {
                   const CameraCalibration& cam,
                   Eigen::Vector3d& in_p) const;
 
+  double Kernel(
+            const cv::Point2f pose,
+            const cv::Mat frame,
+            std::string type) const;
+
   /*
   * @brief IrradianceAnchorPatch_Camera returns irradiance values
   *        of the Anchor Patch position in a camera frame
@@ -181,13 +210,15 @@ bool MarkerGeneration(
                   const CAMState& cam_state,
                   const StateIDType& cam_state_id,
                   CameraCalibration& cam0,
-                  const std::vector<double> photo_r,
-                  std::stringstream& ss) const;
+                  const Eigen::VectorXd& photo_r,
+                  std::stringstream& ss,
+                  cv::Point2f gradientVector,
+                  cv::Point2f residualVector) const;
   /*
-  * @brief projectPixelToPosition uses the calcualted pixels
+  * @brief AnchorPixelToPosition uses the calcualted pixels
   *     of the anchor patch to generate 3D positions of all of em
   */
-inline Eigen::Vector3d projectPixelToPosition(cv::Point2f in_p,
+inline Eigen::Vector3d AnchorPixelToPosition(cv::Point2f in_p,
           const CameraCalibration& cam);
 
   /*
@@ -248,6 +279,26 @@ typedef std::map<FeatureIDType, Feature, std::less<int>,
         Eigen::aligned_allocator<
         std::pair<const FeatureIDType, Feature> > > MapServer;
 
+void Feature::Rhocost(const Eigen::Isometry3d& T_c0_ci,
+    const double x, const Eigen::Vector2d& z1, const Eigen::Vector2d& z2,
+    double& e) const 
+{
+  // Compute hi1, hi2, and hi3 as Equation (37).
+  const double& rho = x;
+
+  Eigen::Vector3d h = T_c0_ci.linear()*
+    Eigen::Vector3d(z1(0), z1(1), 1.0) + rho*T_c0_ci.translation();
+  double& h1 = h(0);
+  double& h2 = h(1);
+  double& h3 = h(2);
+
+  // Predict the feature observation in ci frame.
+  Eigen::Vector2d z_hat(h1/h3, h2/h3);
+
+  // Compute the residual.
+  e = (z_hat-z2).squaredNorm();
+  return;
+}
 
 void Feature::cost(const Eigen::Isometry3d& T_c0_ci,
     const Eigen::Vector3d& x, const Eigen::Vector2d& z,
@@ -260,9 +311,9 @@ void Feature::cost(const Eigen::Isometry3d& T_c0_ci,
 
   Eigen::Vector3d h = T_c0_ci.linear()*
     Eigen::Vector3d(alpha, beta, 1.0) + rho*T_c0_ci.translation();
-  double& h1 = h(0);
-  double& h2 = h(1);
-  double& h3 = h(2);
+  double h1 = h(0);
+  double h2 = h(1);
+  double h3 = h(2);
 
   // Predict the feature observation in ci frame.
   Eigen::Vector2d z_hat(h1/h3, h2/h3);
@@ -272,6 +323,42 @@ void Feature::cost(const Eigen::Isometry3d& T_c0_ci,
   return;
 }
 
+void Feature::RhoJacobian(const Eigen::Isometry3d& T_c0_ci,
+    const double x, const Eigen::Vector2d& z1, const Eigen::Vector2d& z2,
+    Eigen::Matrix<double, 2, 1>& J, Eigen::Vector2d& r,
+    double& w) const 
+{
+
+  const double& rho = x;
+
+  Eigen::Vector3d h = T_c0_ci.linear()*
+    Eigen::Vector3d(z1(0), z2(1), 1.0) + rho*T_c0_ci.translation();
+  double& h1 = h(0);
+  double& h2 = h(1);
+  double& h3 = h(2);
+
+  // Compute the Jacobian.
+  Eigen::Matrix3d W;
+  W.leftCols<2>() = T_c0_ci.linear().leftCols<2>();
+  W.rightCols<1>() = T_c0_ci.translation();
+
+  J(0,0) = -h1/(h3*h3);
+  J(1,0) = -h2/(h3*h3);
+
+  // Compute the residual.
+  Eigen::Vector2d z_hat(h1/h3, h2/h3);
+  r = z_hat - z2;
+
+  // Compute the weight based on the residual.
+  double e = r.norm();
+  if (e <= optimization_config.huber_epsilon)
+    w = 1.0;
+  else
+    w = optimization_config.huber_epsilon / (2*e);
+
+  return;
+}
+
 void Feature::jacobian(const Eigen::Isometry3d& T_c0_ci,
     const Eigen::Vector3d& x, const Eigen::Vector2d& z,
     Eigen::Matrix<double, 2, 3>& J, Eigen::Vector2d& r,
@@ -311,9 +398,9 @@ void Feature::jacobian(const Eigen::Isometry3d& T_c0_ci,
   return;
 }
 
-void Feature::generateInitialGuess(
+double Feature::generateInitialDepth(
     const Eigen::Isometry3d& T_c1_c2, const Eigen::Vector2d& z1,
-    const Eigen::Vector2d& z2, Eigen::Vector3d& p) const 
+    const Eigen::Vector2d& z2) const 
 {
   // Construct a least square problem to solve the depth.
   Eigen::Vector3d m = T_c1_c2.linear() * Eigen::Vector3d(z1(0), z1(1), 1.0);
@@ -328,6 +415,15 @@ void Feature::generateInitialGuess(
 
   // Solve for the depth.
   double depth = (A.transpose() * A).inverse() * A.transpose() * b;
+  return depth;
+}
+
+
+void Feature::generateInitialGuess(
+    const Eigen::Isometry3d& T_c1_c2, const Eigen::Vector2d& z1,
+    const Eigen::Vector2d& z2, Eigen::Vector3d& p) const 
+{
+  double depth = generateInitialDepth(T_c1_c2, z1, z2);
   p(0) = z1(0) * depth;
   p(1) = z1(1) * depth;
   p(2) = depth;
@@ -377,6 +473,26 @@ bool Feature::checkMotion(const CamStateServer& cam_states) const
   else return false;
 }
 
+double Feature::Kernel(
+                const cv::Point2f pose,
+                const cv::Mat frame,
+                std::string type) const
+{
+Eigen::Matrix<double, 3, 3> kernel = Eigen::Matrix<double, 3, 3>::Zero();
+if(type == "Sobel_x")
+  kernel  << -1., 0., 1.,-2., 0., 2. , -1., 0., 1.;
+else if(type == "Sobel_y")
+  kernel << -1., -2., -1., 0., 0., 0., 1., 2., 1.;
+
+double delta = 0;
+int offs = (int)(kernel.rows()-1)/2;
+
+for(int i = 0; i < kernel.rows(); i++)
+  for(int j = 0; j < kernel.cols(); j++)
+    delta += ((float)frame.at<uint8_t>(pose.y+j-offs , pose.x+i-offs))/255 * (float)kernel(j,i);
+
+return delta;
+}
 bool Feature::estimate_FrameIrradiance(
                   const CAMState& cam_state,
                   const StateIDType& cam_state_id,
@@ -532,8 +648,10 @@ bool Feature::VisualizePatch(
                   const CAMState& cam_state,
                   const StateIDType& cam_state_id,
                   CameraCalibration& cam0,
-                  const std::vector<double> photo_r,
-                  std::stringstream& ss) const
+                  const Eigen::VectorXd& photo_r,
+                  std::stringstream& ss,
+                  cv::Point2f gradientVector,
+                  cv::Point2f residualVector) const
 {
 
   double rescale = 1;
@@ -573,45 +691,107 @@ bool Feature::VisualizePatch(
 
   cv::hconcat(cam0.featureVisu, dottedFrame, cam0.featureVisu);
 
-  // irradiance grid anchor
+
+  // patches visualization
   int N = sqrt(anchorPatch_3d.size());
-  int scale = 20;
+  int scale = 30;
   cv::Mat irradianceFrame(anchorImage.size(), CV_8UC3, cv::Scalar(255, 240, 255));
   cv::resize(irradianceFrame, irradianceFrame, cv::Size(), rescale, rescale);
+  
+  // irradiance grid anchor
+  std::stringstream namer;
+  namer << "anchor";
+  cv::putText(irradianceFrame, namer.str() , cvPoint(30, 25), 
+    cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cvScalar(0,0,0), 1, CV_AA);
+
+
   for(int i = 0; i<N; i++)
     for(int j = 0; j<N; j++)
       cv::rectangle(irradianceFrame, 
-                    cv::Point(10+scale*(i+1), 10+scale*j), 
-                    cv::Point(10+scale*i, 10+scale*(j+1)), 
+                    cv::Point(30+scale*(i+1), 30+scale*j), 
+                    cv::Point(30+scale*i, 30+scale*(j+1)), 
                     cv::Scalar(anchorPatch[i*N+j]*255, anchorPatch[i*N+j]*255, anchorPatch[i*N+j]*255),  
                     CV_FILLED);
 
   // irradiance grid projection
+
+  namer.str(std::string());
+  namer << "projection";
+  cv::putText(irradianceFrame, namer.str() , cvPoint(30, 45+scale*N), 
+    cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cvScalar(0,0,0), 1, CV_AA);
+
   for(int i = 0; i<N; i++)
     for(int j = 0; j<N ; j++)
       cv::rectangle(irradianceFrame, 
-                    cv::Point(10+scale*(i+1), 20+scale*(N+j)), 
-                    cv::Point(10+scale*(i), 20+scale*(N+j+1)), 
+                    cv::Point(30+scale*(i+1), 50+scale*(N+j)), 
+                    cv::Point(30+scale*(i), 50+scale*(N+j+1)), 
                     cv::Scalar(projectionPatch[i*N+j]*255, projectionPatch[i*N+j]*255, projectionPatch[i*N+j]*255),  
                     CV_FILLED);
 
+  // true irradiance at feature
+  // get current observation
+
+  namer.str(std::string());
+  namer << "feature";
+  cv::putText(irradianceFrame, namer.str() , cvPoint(30, 65+scale*2*N), 
+    cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cvScalar(0,0,0), 1, CV_AA);
+
+  cv::Point2f p_f(observations.find(cam_state_id)->second(0),observations.find(cam_state_id)->second(1));
+  // move to real pixels
+  p_f = image_handler::distortPoint(p_f, cam0.intrinsics, cam0.distortion_model, cam0.distortion_coeffs);
+
+  for(int i = 0; i<N; i++)
+  {
+    for(int j = 0; j<N ; j++)
+    {
+      float irr = PixelIrradiance(cv::Point2f(p_f.x + (i-(N-1)/2), p_f.y + (j-(N-1)/2)), current_image);
+      cv::rectangle(irradianceFrame, 
+                    cv::Point(30+scale*(i+1), 70+scale*(2*N+j)), 
+                    cv::Point(30+scale*(i), 70+scale*(2*N+j+1)), 
+                    cv::Scalar(irr*255, irr*255, irr*255),  
+                    CV_FILLED);
+    }
+  }
+
   // residual grid projection, positive - red, negative - blue colored 
+  namer.str(std::string());
+  namer << "residual";
+  cv::putText(irradianceFrame, namer.str() , cvPoint(30+scale*N, scale*N/2-5), 
+    cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cvScalar(0,0,0), 1, CV_AA);
+
   for(int i = 0; i<N; i++)
     for(int j = 0; j<N; j++)
-      if(photo_r[i*N+j]>0)
+      if(photo_r(i*N+j)>0)
         cv::rectangle(irradianceFrame, 
-                      cv::Point(20+scale*(N+i+1), 15+scale*(N/2+j)), 
-                      cv::Point(20+scale*(N+i), 15+scale*(N/2+j+1)), 
-                      cv::Scalar(255 - photo_r[i*N+j]*255, 255 - photo_r[i*N+j]*255, 255),  
+                      cv::Point(40+scale*(N+i+1), 15+scale*(N/2+j)), 
+                      cv::Point(40+scale*(N+i), 15+scale*(N/2+j+1)), 
+                      cv::Scalar(255 - photo_r(i*N+j)*255, 255 - photo_r(i*N+j)*255, 255),  
                       CV_FILLED);
       else
         cv::rectangle(irradianceFrame, 
-                      cv::Point(20+scale*(N+i+1), 15+scale*(N/2+j)), 
-                      cv::Point(20+scale*(N+i), 15+scale*(N/2+j+1)), 
-                      cv::Scalar(255, 255 + photo_r[i*N+j]*255, 255 + photo_r[i*N+j]*255), 
+                      cv::Point(40+scale*(N+i+1), 15+scale*(N/2+j)), 
+                      cv::Point(40+scale*(N+i), 15+scale*(N/2+j+1)), 
+                      cv::Scalar(255, 255 + photo_r(i*N+j)*255, 255 + photo_r(i*N+j)*255), 
                       CV_FILLED);
 
-      cv::hconcat(cam0.featureVisu, irradianceFrame, cam0.featureVisu);
+  // gradient arrow
+  /*
+  cv::arrowedLine(irradianceFrame,
+                  cv::Point(30+scale*(N/2 +0.5), 50+scale*(N+(N/2)+0.5)),
+                  cv::Point(30+scale*(N/2+0.5)+scale*gradientVector.x, 50+scale*(N+(N/2)+0.5)+scale*gradientVector.y),
+                  cv::Scalar(100, 0, 255),
+                  1);
+  */
+
+  // residual gradient direction
+  cv::arrowedLine(irradianceFrame,
+                  cv::Point(40+scale*(N+N/2+0.5), 15+scale*((N-0.5))),
+                  cv::Point(40+scale*(N+N/2+0.5)+scale*residualVector.x, 15+scale*(N-0.5)+scale*residualVector.y),
+                  cv::Scalar(0, 255, 175),
+                  3);
+                  
+
+  cv::hconcat(cam0.featureVisu, irradianceFrame, cam0.featureVisu);
       
 /*
   // visualize position of used observations and resulting feature position
@@ -671,6 +851,37 @@ float Feature::PixelIrradiance(cv::Point2f pose, cv::Mat image) const
   return ((float)image.at<uint8_t>(pose.y, pose.x))/255;
 }
 
+cv::Point2f Feature::pixelDistanceAt(
+                  const CAMState& cam_state,
+                  const StateIDType& cam_state_id,
+                  const CameraCalibration& cam,
+                  Eigen::Vector3d& in_p) const
+{
+
+  cv::Point2f cam_p = projectPositionToCamera(cam_state, cam_state_id, cam, in_p);
+
+  // create vector of patch in pixel plane
+  std::vector<cv::Point2f> surroundingPoints;
+  surroundingPoints.push_back(cv::Point2f(cam_p.x+1, cam_p.y));
+  surroundingPoints.push_back(cv::Point2f(cam_p.x-1, cam_p.y));
+  surroundingPoints.push_back(cv::Point2f(cam_p.x, cam_p.y+1));
+  surroundingPoints.push_back(cv::Point2f(cam_p.x, cam_p.y-1));
+
+  std::vector<cv::Point2f> pure;
+  image_handler::undistortPoints(surroundingPoints, 
+                                cam.intrinsics, 
+                                cam.distortion_model, 
+                                cam.distortion_coeffs, 
+                                pure); 
+
+  // returns the absolute pixel distance at pixels one metres away
+  cv::Point2f distance(fabs(pure[0].x - pure[1].x), fabs(pure[2].y - pure[3].y));
+
+  return distance;
+}
+
+
+
 cv::Point2f Feature::projectPositionToCamera(
                   const CAMState& cam_state,
                   const StateIDType& cam_state_id,
@@ -703,7 +914,7 @@ cv::Point2f Feature::projectPositionToCamera(
   return my_p;
 }
 
-Eigen::Vector3d Feature::projectPixelToPosition(cv::Point2f in_p, const CameraCalibration& cam)
+Eigen::Vector3d Feature::AnchorPixelToPosition(cv::Point2f in_p, const CameraCalibration& cam)
 {
   // use undistorted position of point of interest
   // project it back into 3D space using pinhole model
@@ -742,27 +953,19 @@ bool Feature::initializeAnchor(const CameraCalibration& cam, int N)
   cv::Point2f und_pix_p = image_handler::distortPoint(cv::Point2f(u, v), 
                                                     cam.intrinsics, 
                                                     cam.distortion_model, 
-                                                    0);
+                                                    cam.distortion_coeffs);
   // create vector of patch in pixel plane
-  std::vector<cv::Point2f>und_pix_v;
   for(double u_run = -n; u_run <= n; u_run++)
     for(double v_run = -n; v_run <= n; v_run++)
-      und_pix_v.push_back(cv::Point2f(und_pix_p.x+u_run, und_pix_p.y+v_run));
+      anchorPatch_real.push_back(cv::Point2f(und_pix_p.x+u_run, und_pix_p.y+v_run));
 
 
   //create undistorted pure points    
-  std::vector<cv::Point2f> und_v;
-  image_handler::undistortPoints(und_pix_v,
+  image_handler::undistortPoints(anchorPatch_real,
                                 cam.intrinsics,
                                 cam.distortion_model,
-                                0,
-                                und_v);
-
-  //create distorted pixel points
-  anchorPatch_real = image_handler::distortPoints(und_v,
-                                                   cam.intrinsics,
-                                                   cam.distortion_model,
-                                                   cam.distortion_coeffs);
+                                cam.distortion_coeffs,
+                                anchorPatch_ideal);
 
 
   // save anchor position for later visualisaztion
@@ -770,26 +973,170 @@ bool Feature::initializeAnchor(const CameraCalibration& cam, int N)
 
   // save true pixel Patch position
   for(auto point : anchorPatch_real)
-  {
     if(point.x - n < 0 || point.x + n >= cam.resolution(0) || point.y - n < 0 || point.y + n >= cam.resolution(1))
       return false;
-  }
+
   for(auto point : anchorPatch_real)
     anchorPatch.push_back(PixelIrradiance(point, anchorImage));
 
   // project patch pixel to 3D space in camera  coordinate system
-  for(auto point : und_v)
-  {
-    anchorPatch_ideal.push_back(point);
-    anchorPatch_3d.push_back(projectPixelToPosition(point, cam));
-  }
+  for(auto point : anchorPatch_ideal)
+    anchorPatch_3d.push_back(AnchorPixelToPosition(point, cam));
+
   is_anchored = true;
   return true;
 }
 
+bool Feature::initializeRho(const CamStateServer& cam_states) {
+
+  // Organize camera poses and feature observations properly.
+  std::vector<Eigen::Isometry3d,
+    Eigen::aligned_allocator<Eigen::Isometry3d> > cam_poses(0);
+  std::vector<Eigen::Vector2d,
+    Eigen::aligned_allocator<Eigen::Vector2d> > measurements(0);
+
+  for (auto& m : observations) {
+    auto cam_state_iter = cam_states.find(m.first);
+    if (cam_state_iter == cam_states.end()) continue;
+
+    // Add the measurement.
+    measurements.push_back(m.second.head<2>());
+    measurements.push_back(m.second.tail<2>());
+
+    // This camera pose will take a vector from this camera frame
+    // to the world frame.
+    Eigen::Isometry3d cam0_pose;
+    cam0_pose.linear() = quaternionToRotation(
+        cam_state_iter->second.orientation).transpose();
+    cam0_pose.translation() = cam_state_iter->second.position;
+
+    Eigen::Isometry3d cam1_pose;
+    cam1_pose = cam0_pose * CAMState::T_cam0_cam1.inverse();
+
+    cam_poses.push_back(cam0_pose);
+    cam_poses.push_back(cam1_pose);
+  }
+
+  // All camera poses should be modified such that it takes a
+  // vector from the first camera frame in the buffer to this
+  // camera frame.
+  Eigen::Isometry3d T_c0_w = cam_poses[0];
+  T_anchor_w = T_c0_w;
+  for (auto& pose : cam_poses)
+    pose = pose.inverse() * T_c0_w;
+
+  // Generate initial guess
+  double initial_depth = 0;
+  initial_depth = generateInitialDepth(cam_poses[cam_poses.size()-1], measurements[0],
+      measurements[measurements.size()-1]);
+
+  double solution = 1.0/initial_depth;
+
+  // Apply Levenberg-Marquart method to solve for the 3d position.
+  double lambda = optimization_config.initial_damping;
+  int inner_loop_cntr = 0;
+  int outer_loop_cntr = 0;
+  bool is_cost_reduced = false;
+  double delta_norm = 0;
+
+  // Compute the initial cost.
+  double total_cost = 0.0;
+  for (int i = 0; i < cam_poses.size(); ++i) {
+    double this_cost = 0.0;
+    Rhocost(cam_poses[i], solution, measurements[0], measurements[i], this_cost);
+    total_cost += this_cost;
+  }
+
+  // Outer loop.
+  do {
+    Eigen::Matrix<double, 1, 1> A = Eigen::Matrix<double, 1, 1>::Zero();
+    Eigen::Matrix<double, 1, 1>  b = Eigen::Matrix<double, 1, 1>::Zero();
+
+    for (int i = 0; i < cam_poses.size(); ++i) {
+      Eigen::Matrix<double, 2, 1> J;
+      Eigen::Vector2d r;
+      double w;
+
+      RhoJacobian(cam_poses[i], solution, measurements[0], measurements[i], J, r, w);
+
+      if (w == 1) {
+        A += J.transpose() * J;
+        b += J.transpose() * r;
+      } else {
+        double w_square = w * w;
+        A += w_square * J.transpose() * J;
+        b += w_square * J.transpose() * r;
+      }
+    }
+
+    // Inner loop.
+    // Solve for the delta that can reduce the total cost.
+    do {
+      Eigen::Matrix<double, 1, 1> damper = lambda*Eigen::Matrix<double, 1, 1>::Identity();
+      Eigen::Matrix<double, 1, 1> delta = (A+damper).ldlt().solve(b);
+      double new_solution = solution - delta(0,0);
+      delta_norm = delta.norm();
+
+      double new_cost = 0.0;
+      for (int i = 0; i < cam_poses.size(); ++i) {
+        double this_cost = 0.0;
+        Rhocost(cam_poses[i], new_solution, measurements[0], measurements[i], this_cost);
+        new_cost += this_cost;
+      }
+
+      if (new_cost < total_cost) {
+        is_cost_reduced = true;
+        solution = new_solution;
+        total_cost = new_cost;
+        lambda = lambda/10 > 1e-10 ? lambda/10 : 1e-10;
+      } else {
+        is_cost_reduced = false;
+        lambda = lambda*10 < 1e12 ? lambda*10 : 1e12;
+      }
+
+    } while (inner_loop_cntr++ <
+        optimization_config.inner_loop_max_iteration && !is_cost_reduced);
+
+    inner_loop_cntr = 0;
+
+  } while (outer_loop_cntr++ <
+      optimization_config.outer_loop_max_iteration &&
+      delta_norm > optimization_config.estimation_precision);
+
+  // Covert the feature position from inverse depth
+  // representation to its 3d coordinate.
+  Eigen::Vector3d final_position(measurements[0](0)/solution,
+      measurements[0](1)/solution, 1.0/solution);
+
+  // Check if the solution is valid. Make sure the feature
+  // is in front of every camera frame observing it.
+  bool is_valid_solution = true;
+  for (const auto& pose : cam_poses) {
+    Eigen::Vector3d position =
+      pose.linear()*final_position + pose.translation();
+    if (position(2) <= 0) {
+      is_valid_solution = false;
+      break;
+    }
+  }
+
+  //save inverse depth distance from camera
+  anchor_rho = solution;
+
+  // Convert the feature position to the world frame.
+  position = T_c0_w.linear()*final_position + T_c0_w.translation();
+
+  if (is_valid_solution)
+    is_initialized = true;
+
+  return is_valid_solution;
+}
+
+
 bool Feature::initializePosition(const CamStateServer& cam_states) {
 
   // Organize camera poses and feature observations properly.
+
   std::vector<Eigen::Isometry3d,
     Eigen::aligned_allocator<Eigen::Isometry3d> > cam_poses(0);
   std::vector<Eigen::Vector2d,
@@ -927,6 +1274,7 @@ bool Feature::initializePosition(const CamStateServer& cam_states) {
 
   //save inverse depth distance from camera
   anchor_rho = solution(2);
+  std::cout << "from feature: " << anchor_rho << std::endl;
 
   // Convert the feature position to the world frame.
   position = T_c0_w.linear()*final_position + T_c0_w.translation();

include/msckf_vio/image_handler.h

@@ -36,6 +36,15 @@ cv::Point2f distortPoint(
     const cv::Vec4d& intrinsics,
     const std::string& distortion_model,
     const cv::Vec4d& distortion_coeffs);
+
+void undistortPoint(
+    const cv::Point2f& pt_in,
+    const cv::Vec4d& intrinsics,
+    const std::string& distortion_model,
+    const cv::Vec4d& distortion_coeffs,
+    cv::Point2f& pt_out,
+    const cv::Matx33d &rectification_matrix = cv::Matx33d::eye(),
+    const cv::Vec4d &new_intrinsics = cv::Vec4d(1,1,0,0));
 }
 }
 #endif

include/msckf_vio/msckf_vio.h

@@ -195,9 +195,9 @@ class MsckfVio {
     // for a single feature observed at a single camera frame.
     void measurementJacobian(const StateIDType& cam_state_id,
         const FeatureIDType& feature_id,
-        Eigen::Matrix<double, 4, 6>& H_x,
-        Eigen::Matrix<double, 4, 3>& H_f,
-        Eigen::Vector4d& r);
+        Eigen::Matrix<double, 2, 6>& H_x,
+        Eigen::Matrix<double, 2, 3>& H_f,
+        Eigen::Vector2d& r);
     // This function computes the Jacobian of all measurements viewed
     // in the given camera states of this feature.
     void featureJacobian(const FeatureIDType& feature_id,

launch/msckf_vio_debug_tum.launch

@@ -22,7 +22,7 @@
       <param name="PHOTOMETRIC" value="true"/>
 
       <!-- Debugging Flaggs -->
-      <param name="PrintImages" value="false"/>
+      <param name="PrintImages" value="true"/>
       <param name="GroundTruth" value="false"/>
 
       <param name="patch_size_n" value="7"/>

launch/msckf_vio_tum.launch

@@ -21,10 +21,10 @@
       <param name="PHOTOMETRIC" value="true"/>
 
       <!-- Debugging Flaggs -->
-      <param name="PrintImages" value="false"/>
+      <param name="PrintImages" value="true"/>
       <param name="GroundTruth" value="false"/>
 
-      <param name="patch_size_n" value="7"/>
+      <param name="patch_size_n" value="1"/>
       <!-- Calibration parameters -->
       <rosparam command="load" file="$(arg calibration_file)"/>
 

src/image_handler.cpp

@@ -14,6 +14,48 @@ namespace msckf_vio {
 namespace image_handler {
 
 
+
+void undistortPoint(
+    const cv::Point2f& pt_in,
+    const cv::Vec4d& intrinsics,
+    const std::string& distortion_model,
+    const cv::Vec4d& distortion_coeffs,
+    cv::Point2f& pt_out,
+    const cv::Matx33d &rectification_matrix,
+    const cv::Vec4d &new_intrinsics) {
+
+
+  std::vector<cv::Point2f> pts_in;
+  std::vector<cv::Point2f> pts_out;
+  pts_in.push_back(pt_in);
+  if (pts_in.size() == 0) return;
+
+  const cv::Matx33d K(
+      intrinsics[0], 0.0, intrinsics[2],
+      0.0, intrinsics[1], intrinsics[3],
+      0.0, 0.0, 1.0);
+
+  const cv::Matx33d K_new(
+      new_intrinsics[0], 0.0, new_intrinsics[2],
+      0.0, new_intrinsics[1], new_intrinsics[3],
+      0.0, 0.0, 1.0);
+
+  if (distortion_model == "radtan") {
+    cv::undistortPoints(pts_in, pts_out, K, distortion_coeffs,
+                        rectification_matrix, K_new);
+  } else if (distortion_model == "equidistant") {
+    cv::fisheye::undistortPoints(pts_in, pts_out, K, distortion_coeffs,
+                                 rectification_matrix, K_new);
+  } else {
+    ROS_WARN_ONCE("The model %s is unrecognized, use radtan instead...",
+                  distortion_model.c_str());
+    cv::undistortPoints(pts_in, pts_out, K, distortion_coeffs,
+                        rectification_matrix, K_new);
+  }
+  pt_out = pts_out[0];
+  return;
+}
+
 void undistortPoints(
     const std::vector<cv::Point2f>& pts_in,
     const cv::Vec4d& intrinsics,

src/msckf_vio.cpp

@@ -404,8 +404,18 @@ void MsckfVio::imageCallback(
     const sensor_msgs::ImageConstPtr& cam1_img,
     const CameraMeasurementConstPtr& feature_msg)
 {
+
+  if(PRINTIMAGES)
+  {
+    std::cout << "stopped playpack" << std::endl;
+    nh.setParam("/play_bag", false);
+  }
   // Return if the gravity vector has not been set.
-  if (!is_gravity_set) return;
+  if (!is_gravity_set) 
+  {
+    nh.setParam("/play_bag", true);
+    return;
+  }
 
   // Start the system if the first image is received.
   // The frame where the first image is received will be
@@ -430,7 +440,7 @@ void MsckfVio::imageCallback(
   
   //if(ErrState)
   //{
-    batchTruthProcessing(feature_msg->header.stamp.toSec());
+  //batchTruthProcessing(feature_msg->header.stamp.toSec());
     
     if(GROUNDTRUTH)
     {
@@ -454,7 +464,7 @@ void MsckfVio::imageCallback(
     PhotometricStateAugmentation(feature_msg->header.stamp.toSec());
   }
   else
-    stateAugmentation(feature_msg->header.stamp.toSec());
+    PhotometricStateAugmentation(feature_msg->header.stamp.toSec());
   double state_augmentation_time = (
       ros::Time::now()-start_time).toSec();
 
@@ -512,6 +522,13 @@ void MsckfVio::imageCallback(
         publish_time, publish_time/processing_time);
   }
 
+
+  if(PRINTIMAGES)
+  {
+    std::cout << "stopped playpack" << std::endl;
+    nh.setParam("/play_bag", true);
+  }
+
   return;
 }
 
@@ -1175,7 +1192,7 @@ void MsckfVio::PhotometricStateAugmentation(const double& time)
     J * P11 * J.transpose();
 
   // Add photometry P_eta and surrounding Zeros
-  state_server.state_cov(old_rows+6, old_cols+6) = irradiance_frame_bias;
+  state_server.state_cov(old_rows+6, old_cols+6) = 0;
   
   // Fix the covariance to be symmetric
   MatrixXd state_cov_fixed = (state_server.state_cov +
@@ -1232,17 +1249,10 @@ void MsckfVio::PhotometricMeasurementJacobian(
   Matrix3d R_w_c0 = quaternionToRotation(cam_state.orientation);
   const Vector3d& t_c0_w = cam_state.position;
 
-  // Cam1 pose.
-  Matrix3d R_c0_c1 = CAMState::T_cam0_cam1.linear();
-  Matrix3d R_w_c1 = CAMState::T_cam0_cam1.linear() * R_w_c0;
-  Vector3d t_c1_w = t_c0_w - R_w_c1.transpose()*CAMState::T_cam0_cam1.translation();
-
-
   //photometric observation
   std::vector<double> photo_z;
 
   // individual Jacobians
-  Matrix<double, 1, 2> dI_dhj = Matrix<double, 1, 2>::Zero();
   Matrix<double, 2, 3> dh_dCpij = Matrix<double, 2, 3>::Zero();
   Matrix<double, 2, 3> dh_dGpij = Matrix<double, 2, 3>::Zero();
   Matrix<double, 2, 6> dh_dXplj = Matrix<double, 2, 6>::Zero();
@@ -1254,107 +1264,77 @@ void MsckfVio::PhotometricMeasurementJacobian(
   Matrix<double, 3, 3> dCpij_dGpC = Matrix<double, 3, 3>::Zero();
 
   // one line of the NxN Jacobians
-  Eigen::Matrix<double, 1, 1> H_rhoj;
-  Eigen::Matrix<double, 1, 6> H_plj;
-  Eigen::Matrix<double, 1, 6> H_pAj;
-
-  // combined Jacobians
-  Eigen::MatrixXd H_rho(N*N, 1);
-  Eigen::MatrixXd H_pl(N*N, 6);
-  Eigen::MatrixXd H_pA(N*N, 6);
+  Eigen::Matrix<double, 2, 1> H_rho;
+  Eigen::Matrix<double, 2, 6> H_plj;
+  Eigen::Matrix<double, 2, 6> H_pAj;
 
   auto frame = cam0.moving_window.find(cam_state_id)->second.image;
 
   int count = 0;
-  double dx, dy;
 
-  for (auto point : feature.anchorPatch_3d)
-  {
-    Eigen::Vector3d p_c0 = R_w_c0 * (point-t_c0_w);
-    cv::Point2f p_in_c0 = feature.projectPositionToCamera(cam_state, cam_state_id, cam0, point);
+  auto point = feature.anchorPatch_3d[0];
 
-    //add observation
-    photo_z.push_back(feature.PixelIrradiance(p_in_c0, frame));
+  Eigen::Vector3d p_c0 = R_w_c0 * (point-t_c0_w);
+  // add jacobian
+  
+  //dh / d{}^Cp_{ij}
+  dh_dCpij(0, 0) = 1 / p_c0(2);
+  dh_dCpij(1, 1) = 1 / p_c0(2);
+  dh_dCpij(0, 2) = -(p_c0(0))/(p_c0(2)*p_c0(2));
+  dh_dCpij(1, 2) = -(p_c0(1))/(p_c0(2)*p_c0(2));
 
-    // add jacobian
+  dCpij_dGpij = quaternionToRotation(cam_state.orientation);
 
-    // frame derivative calculated convoluting with kernel [-1, 0, 1]
-    dx = feature.PixelIrradiance(cv::Point2f(p_in_c0.x+1, p_in_c0.y), frame) - feature.PixelIrradiance(cv::Point2f(p_in_c0.x-1, p_in_c0.y), frame);
-    dy = feature.PixelIrradiance(cv::Point2f(p_in_c0.x, p_in_c0.y+1), frame) - feature.PixelIrradiance(cv::Point2f(p_in_c0.x, p_in_c0.y-1), frame);
-    dI_dhj(0, 0) = dx;
-    dI_dhj(0, 1) = dy;
-    
-    //dh / d{}^Cp_{ij}
-    dh_dCpij(0, 0) = 1 / p_c0(2);
-    dh_dCpij(1, 1) = 1 / p_c0(2);
-    dh_dCpij(0, 2) = -(p_c0(0))/(p_c0(2)*p_c0(2));
-    dh_dCpij(1, 2) = -(p_c0(1))/(p_c0(2)*p_c0(2));
+  //orientation takes camera frame to world frame
+  dh_dGpij = dh_dCpij * dCpij_dGpij;
 
-    dCpij_dGpij = quaternionToRotation(cam_state.orientation);
+  //dh / d X_{pl}
+  dCpij_dCGtheta = skewSymmetric(p_c0);
+  dCpij_dGpC = -quaternionToRotation(cam_state.orientation);
+  dh_dXplj.block<2, 3>(0, 0) = dh_dCpij * dCpij_dCGtheta;
+  dh_dXplj.block<2, 3>(0, 3) = dh_dCpij * dCpij_dGpC;
 
-    //orientation takes camera frame to world frame, we wa
-    dh_dGpij = dh_dCpij * dCpij_dGpij;
+  //d{}^Gp_P{ij} / \rho_i
+  double rho = feature.anchor_rho;
+  // Isometry T_anchor_w takes a vector in anchor frame to world frame
+  dGpj_drhoj = -feature.T_anchor_w.linear() * Eigen::Vector3d(feature.anchorPatch_ideal[count].x/(rho*rho), feature.anchorPatch_ideal[count].y/(rho*rho), 1/(rho*rho));
 
-    //dh / d X_{pl}
-    dCpij_dCGtheta = skewSymmetric(p_c0);
-    dCpij_dGpC = -quaternionToRotation(cam_state.orientation);
-    dh_dXplj.block<2, 3>(0, 0) = dh_dCpij * dCpij_dCGtheta;
-    dh_dXplj.block<2, 3>(0, 3) = dh_dCpij * dCpij_dGpC;
 
-    //d{}^Gp_P{ij} / \rho_i
-    double rho = feature.anchor_rho;
-    // Isometry T_anchor_w takes a vector in anchor frame to world frame
-    dGpj_drhoj = -feature.T_anchor_w.linear() * Eigen::Vector3d(feature.anchorPatch_ideal[count].x/(rho*rho), feature.anchorPatch_ideal[count].y/(rho*rho), 1/(rho*rho));
 
-    dGpj_XpAj.block<3, 3>(0, 0) = - feature.T_anchor_w.linear()
-                                 * skewSymmetric(Eigen::Vector3d(feature.anchorPatch_ideal[count].x/(rho), 
-                                                   feature.anchorPatch_ideal[count].y/(rho), 
-                                                   1/(rho)));
-    dGpj_XpAj.block<3, 3>(0, 3) = Matrix<double, 3, 3>::Identity();
+  // alternative derivation towards feature
+  Matrix3d dCpc0_dpg = R_w_c0;
+  dGpj_XpAj.block<3, 3>(0, 0) = - feature.T_anchor_w.linear()
+                               * skewSymmetric(Eigen::Vector3d(feature.anchorPatch_ideal[count].x/(rho), 
+                                                 feature.anchorPatch_ideal[count].y/(rho), 
+                                                 1/(rho)));
+  dGpj_XpAj.block<3, 3>(0, 3) = Matrix<double, 3, 3>::Identity();
 
-    // Intermediate Jakobians
-    H_rhoj = dI_dhj * dh_dGpij * dGpj_drhoj; // 1 x 1
-    H_plj = dI_dhj * dh_dXplj; // 1 x 6
-    H_pAj = dI_dhj * dh_dGpij * dGpj_XpAj; // 1 x 6
+  // Intermediate Jakobians
+  H_rho = dh_dGpij * dGpj_drhoj; // 2 x 1
+  H_plj = dh_dXplj; // 2 x 6
+  H_pAj = dh_dGpij * dGpj_XpAj; // 2 x 6
 
-    H_rho.block<1, 1>(count, 0) = H_rhoj;
-    H_pl.block<1, 6>(count, 0) = H_plj;
-    H_pA.block<1, 6>(count, 0) = H_pAj;
-
-    count++;
-  }
   // calculate residual
 
   //observation
-  const Vector4d& z = feature.observations.find(cam_state_id)->second;
-
-  //estimate photometric measurement
-  std::vector<double> estimate_irradiance;
-  std::vector<double> estimate_photo_z;
-  IlluminationParameter estimated_illumination;
-  feature.estimate_FrameIrradiance(cam_state, cam_state_id, cam0, estimate_irradiance, estimated_illumination);
+  const Vector4d& total_z = feature.observations.find(cam_state_id)->second;
+  const Vector2d z = Vector2d(total_z[0], total_z[1]);
+ 
+  VectorXd r_i = VectorXd::Zero(2);
   
-  // calculated here, because we need true 'estimate_irradiance' later for jacobi
-  for (auto& estimate_irradiance_j : estimate_irradiance)
-            estimate_photo_z.push_back (estimate_irradiance_j * 
-                    estimated_illumination.frame_gain * estimated_illumination.feature_gain +
-                    estimated_illumination.frame_bias + estimated_illumination.feature_bias);
+  //calculate residual
 
-  std::vector<double> photo_r;
-  
-  //calculate photom. residual
-  for(int i = 0; i < photo_z.size(); i++)
-    photo_r.push_back(photo_z[i] - estimate_photo_z[i]);
-
-  MatrixXd H_xl = MatrixXd::Zero(N*N, 21+state_server.cam_states.size()*7);
-  MatrixXd H_yl = MatrixXd::Zero(N*N, N*N+state_server.cam_states.size()+1);
+  r_i[0] = z[0] - p_c0(0)/p_c0(2);
+  r_i[1] = z[1] - p_c0(1)/p_c0(2);
 
+  MatrixXd H_xl = MatrixXd::Zero(2, 21+state_server.cam_states.size()*7);
+ 
   // set anchor Jakobi
     // get position of anchor in cam states
 
   auto cam_state_anchor = state_server.cam_states.find(feature.observations.begin()->first);
   int cam_state_cntr_anchor = std::distance(state_server.cam_states.begin(), cam_state_anchor);
-  H_xl.block(0, 21+cam_state_cntr_anchor*7, N*N, 6) = -H_pA; 
+  H_xl.block(0, 21+cam_state_cntr_anchor*7, 2, 6) = H_pAj; 
 
   // set frame Jakobi
     //get position of current frame in cam states
@@ -1362,32 +1342,19 @@ void MsckfVio::PhotometricMeasurementJacobian(
   int cam_state_cntr = std::distance(state_server.cam_states.begin(), cam_state_iter);
   
     // set jakobi of state
-  H_xl.block(0, 21+cam_state_cntr*7, N*N, 6) = -H_pl;
-
-    // set ones for irradiance bias
-  H_xl.block(0, 21+cam_state_cntr*7+6, N*N, 1) = Eigen::ArrayXd::Ones(N*N);
-
-  // set irradiance error Block
-  H_yl.block(0, 0,N*N, N*N) = estimated_illumination.feature_gain * estimated_illumination.frame_gain * Eigen::MatrixXd::Identity(N*N, N*N);
-  
-  // TODO make this calculation more fluent
-  for(int i = 0; i< N*N; i++)
-    H_yl(i, N*N+cam_state_cntr) = estimate_irradiance[i];
-  H_yl.block(0, N*N+state_server.cam_states.size(), N*N, 1) = -H_rho;
+  H_xl.block(0, 21+cam_state_cntr*7, 2, 6) = H_plj;
 
   H_x = H_xl;
-  H_y = H_yl;
+  H_y = H_rho;
+  r = r_i;
 
   //TODO make this more fluent as well
-  count = 0; 
-  for(auto data : photo_r)
-    r[count++] = data;
-  std::stringstream ss;
-  ss << "INFO:" << " anchor: " << cam_state_cntr_anchor << "  frame: " << cam_state_cntr;
   if(PRINTIMAGES)
   {  
-    feature.MarkerGeneration(marker_pub, state_server.cam_states);
-    feature.VisualizePatch(cam_state, cam_state_id, cam0, photo_r, ss);
+    //std::stringstream ss;
+    //ss << "INFO:" << " anchor: " << cam_state_cntr_anchor << "  frame: " << cam_state_cntr;
+    //feature.MarkerGeneration(marker_pub, state_server.cam_states);
+    //feature.VisualizePatch(cam_state, cam_state_id, cam0, photo_r, ss);
   }
 
   return;
@@ -1400,28 +1367,6 @@ void MsckfVio::PhotometricFeatureJacobian(
 {
 
   // stop playing bagfile if printing images
-  if(PRINTIMAGES)
-  {
-    std::cout << "stopped playpack" << std::endl;
-    nh.setParam("/play_bag", false);
-  }
-
-
-  // Errstate
-  calcErrorState();
-  /*
-  std::cout << "--- error state: ---\n " << std::endl;
-  std::cout << "imu orientation:\n " << err_state_server.imu_state.orientation << std::endl;
-  std::cout << "imu position\n" << err_state_server.imu_state.position << std::endl;
-  
-  int count = 0;
-  for(auto cam_state : err_state_server.cam_states)
-  {
-    std::cout << " - cam " << count++ << " - \n" << std::endl;
-    std::cout << "orientation: " << cam_state.second.orientation(0) << cam_state.second.orientation(1) << " " << cam_state.second.orientation(2) << " " << std::endl;
-    std::cout << "position:" << cam_state.second.position(0) << " " << cam_state.second.position(1) << " " << cam_state.second.position(2) << std::endl;
-  }
-  */
 
   const auto& feature = map_server[feature_id];
   
@@ -1433,15 +1378,17 @@ void MsckfVio::PhotometricFeatureJacobian(
     if (feature.observations.find(cam_id) ==
         feature.observations.end()) continue;
 
+    if (feature.observations.find(cam_id) ==
+        feature.observations.begin()) continue;
     valid_cam_state_ids.push_back(cam_id);
   }
 
   int jacobian_row_size = 0;
-  jacobian_row_size = N * N * valid_cam_state_ids.size();
+  jacobian_row_size = 2 * valid_cam_state_ids.size();
 
   MatrixXd H_xi = MatrixXd::Zero(jacobian_row_size,
       21+state_server.cam_states.size()*7);
-  MatrixXd H_yi = MatrixXd::Zero(jacobian_row_size, N*N+state_server.cam_states.size()+1);
+  MatrixXd H_yi = MatrixXd::Zero(jacobian_row_size, 1);
   VectorXd r_i = VectorXd::Zero(jacobian_row_size);
   int stack_cntr = 0;
 
@@ -1449,10 +1396,9 @@ void MsckfVio::PhotometricFeatureJacobian(
 
     MatrixXd H_xl;
     MatrixXd H_yl;
-    Eigen::VectorXd r_l = VectorXd::Zero(N*N);
+    Eigen::VectorXd r_l = VectorXd::Zero(2);
 
     PhotometricMeasurementJacobian(cam_id, feature.id, H_xl, H_yl, r_l);
-
     auto cam_state_iter = state_server.cam_states.find(cam_id);
     int cam_state_cntr = std::distance(
         state_server.cam_states.begin(), cam_state_iter);
@@ -1460,8 +1406,8 @@ void MsckfVio::PhotometricFeatureJacobian(
     // Stack the Jacobians.
     H_xi.block(stack_cntr, 0, H_xl.rows(), H_xl.cols()) = H_xl;
     H_yi.block(stack_cntr, 0, H_yl.rows(), H_yl.cols()) = H_yl;
-    r_i.segment(stack_cntr, N*N) = r_l;
-    stack_cntr += N*N;
+    r_i.segment(stack_cntr, 2) = r_l;
+    stack_cntr += 2;
   }
 
   // Project the residual and Jacobians onto the nullspace
@@ -1470,32 +1416,47 @@ void MsckfVio::PhotometricFeatureJacobian(
   // get Nullspace
   FullPivLU<MatrixXd> lu(H_yi.transpose());
   MatrixXd A_null_space = lu.kernel();
-  /*
-  JacobiSVD<MatrixXd> svd_helper(H_yi, ComputeFullU | ComputeThinV);
-  
-  int sv_size = 0;
-  Eigen::VectorXd singularValues = svd_helper.singularValues();
-  for(int i = 0; i < singularValues.size(); i++)
-    if(singularValues[i] > 1e-12)
-      sv_size++;
 
-  MatrixXd A = svd_helper.matrixU().rightCols(jacobian_row_size - singularValues.size());
-  */
   H_x = A_null_space.transpose() * H_xi;
   r = A_null_space.transpose() * r_i;
 
+  /*
   if(PRINTIMAGES)
   {
+
+  ofstream myfile;
+  myfile.open("/home/raphael/dev/octave/log2octave");
+  myfile << "# Created by Octave 3.8.1, Wed Jun 12 14:36:37 2019 CEST <raphael@raphael-desktop>\n"
+         << "# name: Hx\n"
+         << "# type: matrix\n"
+         << "# rows: " << H_xi.rows() << "\n"
+         << "# columns: " << H_xi.cols() << "\n"
+         << H_xi << endl;
+
+  myfile << "# name: Hy\n"
+         << "# type: matrix\n"
+         << "# rows: " << H_yi.rows() << "\n"  
+         << "# columns: " << H_yi.cols() << "\n"
+         << H_yi << endl;
+
+
+  myfile << "# name: r\n"
+         << "# type: matrix\n"
+         << "# rows: " << r_i.rows() << "\n"  
+         << "# columns: " << 1 << "\n"
+         << r_i << endl;
+
+  myfile.close();
     std::cout << "resume playback" << std::endl;
     nh.setParam("/play_bag", true);
-  }
+  }*/
   return;
 }
 
 void MsckfVio::measurementJacobian(
     const StateIDType& cam_state_id,
     const FeatureIDType& feature_id,
-    Matrix<double, 4, 6>& H_x, Matrix<double, 4, 3>& H_f, Vector4d& r)
+    Matrix<double, 2, 6>& H_x, Matrix<double, 2, 3>& H_f, Vector2d& r)
 {
 
   // Prepare all the required data.
@@ -1514,48 +1475,42 @@ void MsckfVio::measurementJacobian(
   // 3d feature position in the world frame.
   // And its observation with the stereo cameras.
   const Vector3d& p_w = feature.position;
-  const Vector4d& z = feature.observations.find(cam_state_id)->second;
+  const Vector2d& z = feature.observations.find(cam_state_id)->second.topRows(2);
 
   // Convert the feature position from the world frame to
   // the cam0 and cam1 frame.
   Vector3d p_c0 = R_w_c0 * (p_w-t_c0_w);
-  Vector3d p_c1 = R_w_c1 * (p_w-t_c1_w);
+  //Vector3d p_c1 = R_w_c1 * (p_w-t_c1_w);
 
   // Compute the Jacobians.
-  Matrix<double, 4, 3> dz_dpc0 = Matrix<double, 4, 3>::Zero();
+  Matrix<double, 2, 3> dz_dpc0 = Matrix<double, 2, 3>::Zero();
   dz_dpc0(0, 0) = 1 / p_c0(2);
   dz_dpc0(1, 1) = 1 / p_c0(2);
   dz_dpc0(0, 2) = -p_c0(0) / (p_c0(2)*p_c0(2));
   dz_dpc0(1, 2) = -p_c0(1) / (p_c0(2)*p_c0(2));
 
+  /*
   Matrix<double, 4, 3> dz_dpc1 = Matrix<double, 4, 3>::Zero();
   dz_dpc1(2, 0) = 1 / p_c1(2);
   dz_dpc1(3, 1) = 1 / p_c1(2);
   dz_dpc1(2, 2) = -p_c1(0) / (p_c1(2)*p_c1(2));
   dz_dpc1(3, 2) = -p_c1(1) / (p_c1(2)*p_c1(2));
-
+  */
   Matrix<double, 3, 6> dpc0_dxc = Matrix<double, 3, 6>::Zero();
   
   // original jacobi
-  //dpc0_dxc.leftCols(3) = skewSymmetric(p_c0);
-  // my version of calculation
   dpc0_dxc.leftCols(3) = skewSymmetric(p_c0);
-  //dpc0_dxc.leftCols(3) = - skewSymmetric(R_w_c0.transpose() * (t_c0_w - p_w)) * R_w_c0;
   dpc0_dxc.rightCols(3) = -R_w_c0;
 
-  Matrix<double, 3, 6> dpc1_dxc = Matrix<double, 3, 6>::Zero();
-  dpc1_dxc.leftCols(3) = R_c0_c1 * skewSymmetric(p_c0);
-  dpc1_dxc.rightCols(3) = -R_w_c1;
-
   Matrix3d dpc0_dpg = R_w_c0;
   Matrix3d dpc1_dpg = R_w_c1;
 
-  H_x = dz_dpc0*dpc0_dxc + dz_dpc1*dpc1_dxc;
-  H_f = dz_dpc0*dpc0_dpg + dz_dpc1*dpc1_dpg;
+  H_x = dz_dpc0*dpc0_dxc; //+ dz_dpc1*dpc1_dxc;
+  H_f = dz_dpc0*dpc0_dpg; // + dz_dpc1*dpc1_dpg;
 
   // Compute the residual.
-  r = z - Vector4d(p_c0(0)/p_c0(2), p_c0(1)/p_c0(2),
-      p_c1(0)/p_c1(2), p_c1(1)/p_c1(2));
+  r = z - Vector2d(p_c0(0)/p_c0(2), p_c0(1)/p_c0(2));//,
+        //p_c1(0)/p_c1(2), p_c1(1)/p_c1(2));
 
   return;
 }
@@ -1579,19 +1534,19 @@ void MsckfVio::featureJacobian(
   }
 
   int jacobian_row_size = 0;
-  jacobian_row_size = 4 * valid_cam_state_ids.size();
+  jacobian_row_size = 2 * valid_cam_state_ids.size();
 
   MatrixXd H_xj = MatrixXd::Zero(jacobian_row_size,
-      21+state_server.cam_states.size()*6);
+      21+state_server.cam_states.size()*7);
   MatrixXd H_fj = MatrixXd::Zero(jacobian_row_size, 3);
   VectorXd r_j = VectorXd::Zero(jacobian_row_size);
   int stack_cntr = 0;
 
   for (const auto& cam_id : valid_cam_state_ids) {
 
-    Matrix<double, 4, 6> H_xi = Matrix<double, 4, 6>::Zero();
-    Matrix<double, 4, 3> H_fi = Matrix<double, 4, 3>::Zero();
-    Vector4d r_i = Vector4d::Zero();
+    Matrix<double, 2, 6> H_xi = Matrix<double, 2, 6>::Zero();
+    Matrix<double, 2, 3> H_fi = Matrix<double, 2, 3>::Zero();
+    Vector2d r_i = Vector2d::Zero();
     measurementJacobian(cam_id, feature.id, H_xi, H_fi, r_i);
 
     auto cam_state_iter = state_server.cam_states.find(cam_id);
@@ -1599,10 +1554,10 @@ void MsckfVio::featureJacobian(
         state_server.cam_states.begin(), cam_state_iter);
 
     // Stack the Jacobians.
-    H_xj.block<4, 6>(stack_cntr, 21+6*cam_state_cntr) = H_xi;
-    H_fj.block<4, 3>(stack_cntr, 0) = H_fi;
-    r_j.segment<4>(stack_cntr) = r_i;
-    stack_cntr += 4;
+    H_xj.block<2, 6>(stack_cntr, 21+7*cam_state_cntr) = H_xi;
+    H_fj.block<2, 3>(stack_cntr, 0) = H_fi;
+    r_j.segment<2>(stack_cntr) = r_i;
+    stack_cntr += 2;
   }
 
   // Project the residual and Jacobians onto the nullspace
@@ -1623,18 +1578,18 @@ void MsckfVio::featureJacobian(
   FullPivLU<MatrixXd> lu(H_fj.transpose());
   MatrixXd A = lu.kernel();
 
-   H_x = A.transpose() * H_xj;
-   r = A.transpose() * r_j;
+  H_x = A.transpose() * H_xj;
+  r = A.transpose() * r_j;
 
   /*
   ofstream myfile;
   myfile.open ("/home/raphael/dev/MSCKF_ws/log.txt");
-  myfile << "-- residual -- \n" << r << "\n---- H ----\n" << H_x << "\n---- state cov ----\n" << state_server.state_cov <<endl;
+  myfile << "Hx\n" << H_x << "r\n" << r << "from residual estimated error state: " << H_x.colPivHouseholderQr().solve(r) << endl;
   myfile.close();
-  cout << "---------- LOGGED -------- " << endl; 
-  */
-  nh.setParam("/play_bag", false);
 
+  cout << "---------- LOGGED -------- " << endl; 
+  nh.setParam("/play_bag", false);
+  */
   return;
 }
 
@@ -1646,7 +1601,7 @@ void MsckfVio::measurementUpdate(const MatrixXd& H, const VectorXd& r) {
   // complexity as in Equation (28), (29).
   MatrixXd H_thin;
   VectorXd r_thin;
-
+/*
   if (H.rows() > H.cols()) {
     // Convert H to a sparse matrix.
     SparseMatrix<double> H_sparse = H.sparseView();
@@ -1661,8 +1616,8 @@ void MsckfVio::measurementUpdate(const MatrixXd& H, const VectorXd& r) {
     (spqr_helper.matrixQ().transpose() * H).evalTo(H_temp);
     (spqr_helper.matrixQ().transpose() * r).evalTo(r_temp);
 
-    H_thin = H_temp.topRows(21+state_server.cam_states.size()*6);
-    r_thin = r_temp.head(21+state_server.cam_states.size()*6);
+    H_thin = H_temp.topRows(21+state_server.cam_states.size()*7);
+    r_thin = r_temp.head(21+state_server.cam_states.size()*7);
 
     //HouseholderQR<MatrixXd> qr_helper(H);
     //MatrixXd Q = qr_helper.householderQ();
@@ -1670,10 +1625,10 @@ void MsckfVio::measurementUpdate(const MatrixXd& H, const VectorXd& r) {
 
     //H_thin = Q1.transpose() * H;
     //r_thin = Q1.transpose() * r;
-  } else {
+  } else {*/
     H_thin = H;
     r_thin = r;
-  }
+  //}
 
   // Compute the Kalman gain.
   const MatrixXd& P = state_server.state_cov;
@@ -1720,7 +1675,7 @@ void MsckfVio::measurementUpdate(const MatrixXd& H, const VectorXd& r) {
   auto cam_state_iter = state_server.cam_states.begin();
   for (int i = 0; i < state_server.cam_states.size();
       ++i, ++cam_state_iter) {
-    const VectorXd& delta_x_cam = delta_x.segment<6>(21+i*6);
+    const VectorXd& delta_x_cam = delta_x.segment<6>(21+i*7);
     const Vector4d dq_cam = smallAngleQuaternion(delta_x_cam.head<3>());
     cam_state_iter->second.orientation = quaternionMultiplication(
         dq_cam, cam_state_iter->second.orientation);
@@ -1795,7 +1750,7 @@ void MsckfVio::removeLostFeatures() {
         invalid_feature_ids.push_back(feature.id);
         continue;
       } else {
-        if(!feature.initializePosition(state_server.cam_states)) {
+        if(!feature.initializeRho(state_server.cam_states)) {
           invalid_feature_ids.push_back(feature.id);
           continue;
         }
@@ -1812,9 +1767,9 @@ void MsckfVio::removeLostFeatures() {
 
     if(PHOTOMETRIC)
       //just use max. size, as gets shrunken down after anyway
-      jacobian_row_size += N*N*feature.observations.size();
+      jacobian_row_size += 2*feature.observations.size();
     else
-      jacobian_row_size += 4*feature.observations.size() - 3;
+      jacobian_row_size += 2*feature.observations.size() - 3;
     processed_feature_ids.push_back(feature.id);
   }
 
@@ -1831,7 +1786,7 @@ void MsckfVio::removeLostFeatures() {
   if (processed_feature_ids.size() == 0) return;
 
   MatrixXd H_x = MatrixXd::Zero(jacobian_row_size,
-      21+augmentationSize*state_server.cam_states.size());
+      21+7*state_server.cam_states.size());
   VectorXd r = VectorXd::Zero(jacobian_row_size);
   int stack_cntr = 0;
 
@@ -1965,7 +1920,7 @@ void MsckfVio::pruneCamStateBuffer() {
           feature.observations.erase(cam_id);
         continue;
       } else {
-        if(!feature.initializePosition(state_server.cam_states)) {
+        if(!feature.initializeRho(state_server.cam_states)) {
           for (const auto& cam_id : involved_cam_state_ids)
             feature.observations.erase(cam_id);
           continue;
@@ -1982,9 +1937,9 @@ void MsckfVio::pruneCamStateBuffer() {
       }
     }
      if(PHOTOMETRIC)
-      jacobian_row_size += N*N*involved_cam_state_ids.size();
+      jacobian_row_size += 2*involved_cam_state_ids.size();
     else
-      jacobian_row_size += 4*involved_cam_state_ids.size() - 3;
+      jacobian_row_size += 2*involved_cam_state_ids.size() - 3;
   }
 
   //cout << "jacobian row #: " << jacobian_row_size << endl;
@@ -1992,7 +1947,7 @@ void MsckfVio::pruneCamStateBuffer() {
   // Compute the Jacobian and residual.
   MatrixXd H_xj;
   VectorXd r_j;
-  MatrixXd H_x = MatrixXd::Zero(jacobian_row_size, 21+augmentationSize*state_server.cam_states.size());
+  MatrixXd H_x = MatrixXd::Zero(jacobian_row_size, 21+7*state_server.cam_states.size());
   VectorXd r = VectorXd::Zero(jacobian_row_size);
   int stack_cntr = 0;
   for (auto& item : map_server) {
@@ -2037,8 +1992,8 @@ void MsckfVio::pruneCamStateBuffer() {
   for (const auto& cam_id : rm_cam_state_ids) {
     int cam_sequence = std::distance(state_server.cam_states.begin(),
         state_server.cam_states.find(cam_id));
-    int cam_state_start = 21 + augmentationSize*cam_sequence;
-    int cam_state_end = cam_state_start + augmentationSize;
+    int cam_state_start = 21 + 7*cam_sequence;
+    int cam_state_end = cam_state_start + 7;
 
 
     // Remove the corresponding rows and columns in the state
@@ -2059,10 +2014,10 @@ void MsckfVio::pruneCamStateBuffer() {
             state_server.state_cov.cols()-cam_state_end);
 
       state_server.state_cov.conservativeResize(
-          state_server.state_cov.rows()-augmentationSize, state_server.state_cov.cols()-augmentationSize);
+          state_server.state_cov.rows()-7, state_server.state_cov.cols()-7);
     } else {
       state_server.state_cov.conservativeResize(
-          state_server.state_cov.rows()-augmentationSize, state_server.state_cov.cols()-augmentationSize);
+          state_server.state_cov.rows()-7, state_server.state_cov.cols()-7);
     }
 
     // Remove this camera state in the state vector.