diff --git a/rct_examples/CMakeLists.txt b/rct_examples/CMakeLists.txt
index 140e8a3c..f1d98e3f 100644
--- a/rct_examples/CMakeLists.txt
+++ b/rct_examples/CMakeLists.txt
@@ -111,6 +111,15 @@ set_target_properties(${PROJECT_NAME}_noise_qualification_2d PROPERTIES OUTPUT_N
 add_dependencies(${PROJECT_NAME}_noise_qualification_2d ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
 target_link_libraries(${PROJECT_NAME}_noise_qualification_2d ${catkin_LIBRARIES} rct::rct_optimizations rct::rct_image_tools)
 
+#Executable for testing camera noise
+add_executable(${PROJECT_NAME}_kinematic_calibration src/tools/kinematic_calibration.cpp)
+
+set_target_properties(${PROJECT_NAME}_kinematic_calibration PROPERTIES OUTPUT_NAME kinematic_calibration PREFIX "")
+
+add_dependencies(${PROJECT_NAME}_kinematic_calibration ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
+
+target_link_libraries(${PROJECT_NAME}_kinematic_calibration ${catkin_LIBRARIES} rct::rct_optimizations rct::rct_image_tools)
+
 #############
 ## Testing ##
 #############
diff --git a/rct_examples/src/tools/kinematic_calibration.cpp b/rct_examples/src/tools/kinematic_calibration.cpp
new file mode 100644
index 00000000..50db442f
--- /dev/null
+++ b/rct_examples/src/tools/kinematic_calibration.cpp
@@ -0,0 +1,325 @@
+#include <rct_optimizations/dh_chain_kinematic_calibration.h>
+#include <rct_optimizations/pnp.h>
+#include <rct_image_tools/image_observation_finder.h>
+#include <rct_ros_tools/parameter_loaders.h>
+#include <rct_ros_tools/exceptions.h>
+#include <rct_ros_tools/target_loaders.h>
+
+#include <boost/accumulators/accumulators.hpp>
+#include <boost/accumulators/statistics.hpp>
+#include <opencv2/highgui.hpp>
+#include <random>
+#include <ros/ros.h>
+#include <yaml-cpp/yaml.h>
+
+using namespace rct_optimizations;
+using namespace rct_ros_tools;
+using namespace rct_image_tools;
+
+CircleDetectorParams createDetectorParams()
+{
+  CircleDetectorParams params;
+  params.minThreshold = 50;
+  params.maxThreshold = 220;
+  params.nThresholds = 20;
+
+  params.minRepeatability = 1;
+  params.circleInclusionRadius = 10;
+  params.maxRadiusDiff = 10;
+
+  params.maxAverageEllipseError = 0.002;
+
+  params.filterByArea = true;
+  params.minArea = 200.0;
+  params.maxArea = 10000.0;
+
+  params.filterByConvexity = false;
+  params.minConvexity = 0.95f;
+  params.maxConvexity = 1.05f;
+
+  params.filterByColor = false;
+  params.circleColor = 0;
+
+  params.filterByInertia = false;
+  params.filterByCircularity = false;
+
+  return params;
+}
+
+KinObservation2D3D::Set loadMeasurements(const std::string& filename, const ModifiedCircleGridObservationFinder& target_finder)
+{
+  KinObservation2D3D::Set measurements;
+  std::vector<Eigen::Vector3d> target_points = target_finder.target().createPoints();
+
+  // Load the data YAML file
+  YAML::Node n = YAML::LoadFile(filename);
+  measurements.reserve(n.size());
+
+  // Loop over all observations
+  for (auto it = n.begin(); it != n.end(); ++it)
+  {
+    KinObservation2D3D observation;
+
+    // Load the image
+    std::string image_file = "ros/" + it->first.as<std::string>() + "_image.png";
+    cv::Mat image = cv::Scalar::all(255) - cv::imread(image_file, CV_LOAD_IMAGE_COLOR);  // TODO: Is CV_LOAD_IMAGE_COLOR needed?
+    if (image.data == NULL)
+      throw std::runtime_error("File failed to load or does not exist: " + image_file);
+
+    // Find the target
+    CircleDetectorParams detector_params = createDetectorParams();
+    CircleDetector detector(detector_params);
+    cv::Mat debug_image = detector.drawDetectedCircles(image);
+    cv::namedWindow("circle_detection_debug", 0);
+    cv::resizeWindow("circle_detection_debug", 1000, 800);
+    cv::imshow("circle_detection_debug", debug_image);
+    cv::waitKey();
+
+    auto features = target_finder.findObservations(image, &detector_params);
+    if (!features)
+    {
+      std::cout << "Failed to find observations for image '" << image_file << "'" << std::endl;
+      cv::imshow("circle_detection_debug", image);
+      cv::waitKey();
+      continue;
+    }
+    else
+    {
+      cv::Mat obs_image = target_finder.drawObservations(image, features.get());
+      cv::imshow("circle_detection_debug", obs_image);
+
+      bool accepted = false;
+      cv::MouseCallback cb = [](int event, int x, int y, int flags, void* userdata) {
+        if(event == cv::EVENT_FLAG_LBUTTON)
+        {
+          bool* val = reinterpret_cast<bool*>(userdata);
+          *val = true;
+        }
+      };
+
+      cv::setMouseCallback("circle_detection_debug", cb, &accepted);
+      cv::waitKey();
+
+      if (!accepted)
+        continue;
+
+      std::cout << "Image accepted!" << std::endl;
+    }
+
+    // Create the correspondences
+    observation.correspondence_set.reserve(features->size());
+    for (std::size_t i = 0; i < target_points.size(); ++i)
+    {
+      Correspondence2D3D corr;
+      corr.in_image = features->at(i);
+      corr.in_target = target_points.at(i);
+      observation.correspondence_set.push_back(corr);
+    }
+
+    // Target chain joints
+    {
+      YAML::Node joints = it->second["camera_joints"];
+      observation.camera_chain_joints.resize(joints.size());
+      for (std::size_t i = 0; i < joints.size(); ++i)
+      {
+        observation.camera_chain_joints[i] = joints[i].as<double>();
+      }
+    }
+
+    // Camera chain joints
+    {
+      YAML::Node joints = it->second["target_joints"];
+      observation.target_chain_joints.resize(joints.size());
+      for (std::size_t i = 0; i < joints.size(); ++i)
+      {
+        observation.target_chain_joints[i] = joints[i].as<double>();
+      }
+    }
+
+    // Add the observation to the set
+    measurements.push_back(observation);
+  }
+
+  return measurements;
+}
+
+DHChain createTwoAxisPositioner()
+{
+  std::vector<DHTransform> transforms;
+  transforms.reserve(2);
+
+  Eigen::Vector4d p1, p2;
+  p1 << 0.0, 0.0, 0.0, -M_PI / 2.0;
+  p2 << -0.475, -M_PI / 2.0, 0.0, 0.0;
+
+  // Add the first DH transform
+  {
+    DHTransform t(p1, DHJointType::REVOLUTE, "j1");
+    t.max = M_PI;
+    t.min = -M_PI;
+    transforms.push_back(t);
+  }
+  // Add the second DH transform
+  {
+    DHTransform dh_transform(p2, DHJointType::REVOLUTE, "j2");
+    dh_transform.max = 2.0 * M_PI;
+    dh_transform.min = -2.0 * M_PI;
+    transforms.push_back(dh_transform);
+  }
+
+  // Set an arbitrary base offset
+  Eigen::Isometry3d base_offset(Eigen::Isometry3d::Identity());
+  base_offset.translate(Eigen::Vector3d(2.2, 0.0, 1.6));
+  base_offset.rotate(Eigen::AngleAxisd(M_PI / 2.0, Eigen::Vector3d::UnitX()));
+
+  return DHChain(transforms, base_offset);
+}
+
+void test(const DHChain& camera_chain, const DHChain& target_chain,
+          const KinematicCalibrationResult& result, const KinObservation2D3D::Set& observations,
+          const CameraIntrinsics& intr)
+{
+  namespace ba = boost::accumulators;
+  ba::accumulator_set<double, ba::stats<ba::tag::mean, ba::tag::variance>> pos_acc;
+  ba::accumulator_set<double, ba::stats<ba::tag::mean, ba::tag::variance>> ori_acc;
+
+  for (const KinObservation2D3D& obs : observations)
+  {
+    // Build the transforms from the camera chain base out to the camera
+    Eigen::Isometry3d camera_chain_fk = camera_chain.getFK<double>(obs.camera_chain_joints);
+    Eigen::Isometry3d camera_base_to_camera = camera_chain_fk * result.camera_mount_to_camera;
+
+    // Build the transforms from the camera chain base out to the target
+    Eigen::Isometry3d target_chain_fk = target_chain.getFK<double>(obs.target_chain_joints);
+    Eigen::Isometry3d camera_base_to_target =
+        result.camera_base_to_target_base * target_chain_fk * result.target_mount_to_target;
+
+    // Now that we have two transforms in the same frame, get the target point in the camera frame
+    Eigen::Isometry3d camera_to_target = camera_base_to_camera.inverse() * camera_base_to_target;
+
+    PnPProblem pnp;
+    pnp.camera_to_target_guess = camera_to_target;
+    pnp.correspondences = obs.correspondence_set;
+    pnp.intr = intr;
+    PnPResult result = optimize(pnp);
+
+    if(result.converged)
+    {
+      // Compare
+      Eigen::Isometry3d diff = camera_to_target.inverse() * result.camera_to_target;
+      pos_acc(diff.translation().norm());
+      ori_acc(Eigen::Quaterniond(camera_to_target.linear()).angularDistance(Eigen::Quaterniond(result.camera_to_target.linear())));
+    }
+    else
+    {
+      std::cout << "PnP optimization failed" << std::endl;
+    }
+  }
+
+  std::cout << "Position Difference Mean: " << ba::mean(pos_acc) << std::endl;
+  std::cout << "Position Difference Std. Dev.: " << std::sqrt(ba::variance(pos_acc)) << std::endl;
+
+  std::cout << "Orientation Difference Mean: " << ba::mean(ori_acc) << std::endl;
+  std::cout << "Orientation difference Std. Dev.: " << std::sqrt(ba::variance(ori_acc)) << std::endl;
+}
+
+template <typename T>
+bool get(const ros::NodeHandle& nh, const std::string& key, T& val)
+{
+  if (!nh.getParam(key, val))
+  {
+    ROS_ERROR_STREAM("Failed to get '" << key << "' parameter");
+    return false;
+  }
+  return true;
+}
+
+int main(int argc, char** argv)
+{
+  if (argc != 6)
+  {
+    std::cout << "Incorrect number of arguments: " << argc << std::endl;
+    return -1;
+  }
+
+  ModifiedCircleGridTarget target = TargetLoader<ModifiedCircleGridTarget>::load(argv[2]);
+  ModifiedCircleGridObservationFinder target_finder(target);
+
+  // Create the problem
+  KinematicCalibrationProblem2D3D problem(DHChain({}), createTwoAxisPositioner());
+
+  // Load the camera intrinsics
+  problem.intr = loadIntrinsics(argv[3]);
+
+  // Load the pose guesses
+  problem.target_mount_to_target_guess = loadPose(argv[4]);
+  problem.camera_mount_to_camera_guess = loadPose(argv[5]);
+
+  // Load the observations
+  KinObservation2D3D::Set measurements = loadMeasurements(argv[1], target_finder);
+
+  problem.observations = measurements;
+  std::cout << "Performing calibration with " << problem.observations.size() << " observations" << std::endl;
+
+  problem.camera_chain_dh_stdev_expectation = 0.001;
+  problem.target_chain_dh_stdev_expectation = 0.005;
+
+  // Mask a few DH parameters in the target chain (index 1)
+  {
+    Eigen::Matrix<bool, Eigen::Dynamic, 4> mask =
+        Eigen::Matrix<bool, Eigen::Dynamic, 4>::Constant(problem.target_chain.dof(), 4, false);
+
+    // Mask the last row because they duplicate the target mount to target transform
+    mask.bottomRows(1) << true, true, true, true;
+
+    // Add the mask to the problem
+    problem.mask.at(1) = createDHMask(mask);
+  }
+
+  /* Mask the camera base to target base transform (duplicated by target mount to target transform when
+   * the target chain has no joints */
+  problem.mask.at(6) = { 0, 1, 2 };
+  problem.mask.at(7) = { 0, 1, 2 };
+
+  ceres::Solver::Options options;
+  options.num_threads = 4;
+  options.minimizer_progress_to_stdout = true;
+  options.max_num_iterations = 500;
+  options.use_nonmonotonic_steps = true;
+  options.function_tolerance = 1.0e-12;
+  options.parameter_tolerance = 1.0e-20;
+  options.gradient_tolerance = 1.0e-16;
+
+  KinematicCalibrationResult result = optimize(problem, options);
+
+  Eigen::IOFormat fmt(6, 0, "|", "\n", "|", "|");
+
+  std::stringstream ss;
+  ss << "\nCalibration " << (result.converged ? "did" : "did not") << " converge\n";
+  ss << "Initial cost per observation: " << std::sqrt(result.initial_cost_per_obs) << "\n";
+  ss << "Final cost per observation: " << std::sqrt(result.final_cost_per_obs) << "\n";
+
+  ss << "\nCamera mount to camera\n" << result.camera_mount_to_camera.matrix().format(fmt) << "\n";
+  ss << "Euler ZYX: " << result.camera_mount_to_camera.rotation().eulerAngles(2, 1, 0).transpose().format(fmt) << "\n";
+
+  ss << "\nTarget mount to target\n" << result.target_mount_to_target.matrix().format(fmt) << "\n";
+  ss << "Euler ZYX: " << result.target_mount_to_target.rotation().eulerAngles(2, 1, 0).transpose().format(fmt) << "\n";
+
+  ss << "\nTarget chain DH parameter offsets\n" << result.target_chain_dh_offsets.matrix().format(fmt) << "\n";
+  ss << "\nCamera chain DH parameter offsets\n" << result.camera_chain_dh_offsets.matrix().format(fmt) << "\n";
+  ss << result.covariance.printCorrelationCoeffAboveThreshold(0.5);
+
+  std::cout << ss.str() << std::endl;
+
+  // Test the result by moving the robot around to a lot of positions and seeing of the results match
+  DHChain optimized_camera_chain(problem.camera_chain, result.camera_chain_dh_offsets);
+  DHChain optimized_target_chain(problem.target_chain, result.target_chain_dh_offsets);
+
+  std::cout << "Optimized camera chain DH parameters\n" << optimized_camera_chain.getDHTable().format(fmt) << std::endl;
+  std::cout << "Optimized target chain DH parameters\n" << optimized_target_chain.getDHTable().format(fmt) << std::endl;
+
+  std::cout << "\nValidating calibration with " << measurements.size() << " observations" << std::endl;
+  test(optimized_camera_chain, optimized_target_chain, result, measurements, problem.intr);
+
+  return 0;
+}
diff --git a/rct_optimizations/include/rct_optimizations/dh_chain.h b/rct_optimizations/include/rct_optimizations/dh_chain.h
index 8f6608be..fdf35bb1 100644
--- a/rct_optimizations/include/rct_optimizations/dh_chain.h
+++ b/rct_optimizations/include/rct_optimizations/dh_chain.h
@@ -119,6 +119,8 @@ class DHChain
   DHChain(std::vector<DHTransform> transforms,
           const Eigen::Isometry3d& base_offset = Eigen::Isometry3d::Identity());
 
+  DHChain(const DHChain& rhs, const Eigen::MatrixX4d& dh_offsets);
+
   /**
    * @brief Calculates forward kinematics for the chain with the joints provided.
    * Note: the transform to the n-th link is calculated, where n is the size of @ref joint_values
@@ -199,6 +201,12 @@ class DHChain
    */
   std::vector<std::array<std::string, 4>> getParamLabels() const;
 
+  /**
+   * @brief Gets the base offset of the transform
+   * @return
+   */
+  Eigen::Isometry3d getBaseOffset() const;
+
 protected:
   /** @brief The DH transforms that make up the chain */
   std::vector<DHTransform> transforms_;
diff --git a/rct_optimizations/include/rct_optimizations/dh_chain_kinematic_calibration.h b/rct_optimizations/include/rct_optimizations/dh_chain_kinematic_calibration.h
index 884ee77a..3be9ef22 100644
--- a/rct_optimizations/include/rct_optimizations/dh_chain_kinematic_calibration.h
+++ b/rct_optimizations/include/rct_optimizations/dh_chain_kinematic_calibration.h
@@ -66,6 +66,9 @@ struct KinematicCalibrationProblem2D3D
    */
   std::array<std::vector<int>, 8> mask;
 
+  double camera_chain_dh_stdev_expectation;
+  double target_chain_dh_stdev_expectation;
+
   std::string label_camera_mount_to_camera = "camera_mount_to_camera";
   std::string label_target_mount_to_target = "target_mount_to_target";
   std::string label_camera_base_to_target = "camera_base_to_target";
@@ -237,7 +240,8 @@ class DualDHChainCost2D3D
   Eigen::VectorXd target_chain_joints_;
 };
 
-KinematicCalibrationResult optimize(const KinematicCalibrationProblem2D3D &problem);
+KinematicCalibrationResult optimize(const KinematicCalibrationProblem2D3D &problem,
+                                    const ceres::Solver::Options& options = ceres::Solver::Options());
 
 } // namespace rct_optimizations
 
diff --git a/rct_optimizations/src/rct_optimizations/dh_chain.cpp b/rct_optimizations/src/rct_optimizations/dh_chain.cpp
index f1a75c98..ec7f8cd8 100644
--- a/rct_optimizations/src/rct_optimizations/dh_chain.cpp
+++ b/rct_optimizations/src/rct_optimizations/dh_chain.cpp
@@ -47,6 +47,18 @@ DHChain::DHChain(std::vector<DHTransform> transforms,
 {
 }
 
+DHChain::DHChain(const DHChain& other, const Eigen::MatrixX4d& dh_offsets)
+{
+  transforms_.reserve(other.transforms_.size());
+  for (std::size_t i = 0; i < other.transforms_.size(); ++i)
+  {
+    const DHTransform& transform = other.transforms_.at(i);
+    transforms_.emplace_back(transform.params + dh_offsets.row(i).transpose(), transform.type);
+  }
+
+  base_offset_ = other.base_offset_;
+}
+
 Eigen::VectorXd DHChain::createUniformlyRandomPose() const
 {
   Eigen::VectorXd joints(transforms_.size());
@@ -93,5 +105,9 @@ std::vector<std::array<std::string, 4>> DHChain::getParamLabels() const
   return out;
 }
 
+Eigen::Isometry3d DHChain::getBaseOffset() const
+{
+  return base_offset_;
+}
 
 } // namespace rct_optimizations
diff --git a/rct_optimizations/src/rct_optimizations/dh_chain_kinematic_calibration.cpp b/rct_optimizations/src/rct_optimizations/dh_chain_kinematic_calibration.cpp
index 6d97a6e9..485d604c 100644
--- a/rct_optimizations/src/rct_optimizations/dh_chain_kinematic_calibration.cpp
+++ b/rct_optimizations/src/rct_optimizations/dh_chain_kinematic_calibration.cpp
@@ -23,7 +23,8 @@ Eigen::Isometry3d createTransform(const Eigen::Vector3d& t, const Eigen::Vector3
   return result;
 }
 
-KinematicCalibrationResult optimize(const KinematicCalibrationProblem2D3D &params)
+KinematicCalibrationResult optimize(const KinematicCalibrationProblem2D3D &params,
+                                    const ceres::Solver::Options& options)
 {
   // Initialize the optimization variables
   // Camera mount to camera (cm_to_c) unnormalized angle axis and translation
@@ -168,14 +169,14 @@ KinematicCalibrationResult optimize(const KinematicCalibrationProblem2D3D &param
   addSubsetParameterization(problem, params.mask, tmp);
 
   // Add a cost to drive the camera chain DH parameters towards an expected mean
-  if (params.camera_chain.dof() != 0)
+  if (params.camera_chain.dof() != 0 && !problem.IsParameterBlockConstant(parameters[0]))
   {
     Eigen::ArrayXXd mean(
       Eigen::ArrayXXd::Zero(camera_chain_dh_offsets.rows(), camera_chain_dh_offsets.cols()));
 
     Eigen::ArrayXXd stdev(Eigen::ArrayXXd::Constant(camera_chain_dh_offsets.rows(),
                                                     camera_chain_dh_offsets.cols(),
-                                                    1.0e-3));
+                                                    params.target_chain_dh_stdev_expectation));
 
     auto *fn = new MaximumLikelihood(mean, stdev);
     auto *cost_block = new ceres::DynamicAutoDiffCostFunction<MaximumLikelihood>(fn);
@@ -186,14 +187,14 @@ KinematicCalibrationResult optimize(const KinematicCalibrationProblem2D3D &param
   }
 
   // Add a cost to drive the target chain DH parameters towards an expected mean
-  if (params.target_chain.dof() != 0)
+  if (params.target_chain.dof() != 0 && !problem.IsParameterBlockConstant(parameters[1]))
   {
     Eigen::ArrayXXd mean(
       Eigen::ArrayXXd::Zero(target_chain_dh_offsets.rows(), target_chain_dh_offsets.cols()));
 
     Eigen::ArrayXXd stdev(Eigen::ArrayXXd::Constant(target_chain_dh_offsets.rows(),
-                                                   target_chain_dh_offsets.cols(),
-                                                   1.0e-3));
+                                                    target_chain_dh_offsets.cols(),
+                                                    params.target_chain_dh_stdev_expectation));
 
     auto *fn = new MaximumLikelihood(mean, stdev);
     auto *cost_block = new ceres::DynamicAutoDiffCostFunction<MaximumLikelihood>(fn);
@@ -203,14 +204,8 @@ KinematicCalibrationResult optimize(const KinematicCalibrationProblem2D3D &param
     problem.AddResidualBlock(cost_block, nullptr, target_chain_dh_offsets.data());
   }
 
-  // Setup the Ceres optimization parameters
-  ceres::Solver::Options options;
-  options.max_num_iterations = 150;
-  options.num_threads = 4;
-  options.minimizer_progress_to_stdout = true;
-  ceres::Solver::Summary summary;
-
   // Solve the optimization
+  ceres::Solver::Summary summary;
   ceres::Solve(options, &problem, &summary);
 
   // Report and save the results