Added AggregateReadings class and local functors.h header. Implemented the derivative of ExpmapDerivative correction.

gtsam/navigation/AggregateImuReadings.cpp

@@ -0,0 +1,209 @@
+/* ----------------------------------------------------------------------------
+
+ * 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    AggregateImuReadings.cpp
+ * @brief   Integrates IMU readings on the NavState tangent space
+ * @author  Frank Dellaert
+ */
+
+#include <gtsam/navigation/AggregateImuReadings.h>
+#include <gtsam/navigation/functors.h>
+#include <gtsam/nonlinear/ExpressionFactor.h>
+#include <gtsam/nonlinear/NonlinearFactorGraph.h>
+#include <gtsam/linear/GaussianBayesNet.h>
+#include <gtsam/linear/GaussianFactorGraph.h>
+#include <gtsam/inference/Symbol.h>
+
+#include <boost/assign/std/list.hpp>
+
+#include <cmath>
+
+using namespace std;
+using namespace boost::assign;
+
+namespace gtsam {
+
+using symbol_shorthand::T;  // for theta
+using symbol_shorthand::P;  // for position
+using symbol_shorthand::V;  // for velocity
+
+static const Symbol kBiasKey('B', 0);
+
+SharedDiagonal AggregateImuReadings::discreteAccelerometerNoiseModel(
+    double dt) const {
+  return noiseModel::Diagonal::Sigmas(accelerometerNoiseModel_->sigmas() /
+                                      std::sqrt(dt));
+}
+
+SharedDiagonal AggregateImuReadings::discreteGyroscopeNoiseModel(
+    double dt) const {
+  return noiseModel::Diagonal::Sigmas(gyroscopeNoiseModel_->sigmas() /
+                                      std::sqrt(dt));
+}
+
+NonlinearFactorGraph AggregateImuReadings::createGraph(
+    const Vector3_& theta_, const Vector3_& pos_, const Vector3_& vel_,
+    const Vector3& measuredAcc, const Vector3& measuredOmega, double dt) const {
+  NonlinearFactorGraph graph;
+  Expression<Bias> bias_(kBiasKey);
+  Vector3_ theta_plus_(T(k_ + 1)), pos_plus_(P(k_ + 1)), vel_plus_(V(k_ + 1));
+
+  Vector3_ omega_(PredictAngularVelocity(dt), theta_, theta_plus_);
+  Vector3_ measuredOmega_(boost::bind(&Bias::correctGyroscope, _1, _2, _3, _4),
+                          bias_, omega_);
+  auto gyroModel = discreteGyroscopeNoiseModel(dt);
+  graph.addExpressionFactor(gyroModel, measuredOmega, measuredOmega_);
+
+  Vector3_ averageVelocity_(averageVelocity, vel_, vel_plus_);
+  Vector3_ defect_(PositionDefect(dt), pos_, pos_plus_, averageVelocity_);
+  static const auto constrModel = noiseModel::Constrained::All(3);
+  static const Vector3 kZero(Vector3::Zero());
+  graph.addExpressionFactor(constrModel, kZero, defect_);
+
+  Vector3_ acc_(PredictAcceleration(dt), vel_, vel_plus_, theta_);
+  Vector3_ measuredAcc_(
+      boost::bind(&Bias::correctAccelerometer, _1, _2, _3, _4), bias_, acc_);
+  auto accModel = discreteAccelerometerNoiseModel(dt);
+  graph.addExpressionFactor(accModel, measuredAcc, measuredAcc_);
+
+  return graph;
+}
+
+AggregateImuReadings::SharedBayesNet AggregateImuReadings::initPosterior(
+    const Vector3& measuredAcc, const Vector3& measuredOmega, double dt) {
+  static const Vector3 kZero(Vector3::Zero());
+  static const Vector3_ zero_(kZero);
+
+  // We create a factor graph and then compute P(zeta|bias)
+  auto graph = createGraph(zero_, zero_, zero_, measuredAcc, measuredOmega, dt);
+
+  // These values are exact the first time
+  values.insert<Vector3>(T(k_ + 1), measuredOmega * dt);
+  values.insert<Vector3>(P(k_ + 1), measuredAcc * (0.5 * dt * dt));
+  values.insert<Vector3>(V(k_ + 1), measuredAcc * dt);
+  values.insert<Bias>(kBiasKey, estimatedBias_);
+  auto linear_graph = graph.linearize(values);
+
+  // eliminate all but biases
+  // NOTE(frank): After this, posterior_k_ contains P(zeta(1)|bias)
+  Ordering keys = list_of(T(k_ + 1))(P(k_ + 1))(V(k_ + 1));
+  return linear_graph->eliminatePartialSequential(keys, EliminateQR).first;
+}
+
+AggregateImuReadings::SharedBayesNet AggregateImuReadings::integrateCorrected(
+    const Vector3& measuredAcc, const Vector3& measuredOmega, double dt) {
+  static const Vector3 kZero(Vector3::Zero());
+  static const auto constrModel = noiseModel::Constrained::All(3);
+
+  // We create a factor graph and then compute P(zeta|bias)
+  auto graph = createGraph(Vector3_(T(k_)), Vector3_(P(k_)), Vector3_(V(k_)),
+                           measuredAcc, measuredOmega, dt);
+
+  // Get current estimates
+  const Vector3 theta = values.at<Vector3>(T(k_));
+  const Vector3 pos = values.at<Vector3>(P(k_));
+  const Vector3 vel = values.at<Vector3>(V(k_));
+
+  // Calculate exact solution: means we do not have to update values
+  // TODO(frank): Expmap and ExpmapDerivative are called again :-(
+  const Vector3 correctedAcc = measuredAcc - estimatedBias_.accelerometer();
+  const Vector3 correctedOmega = measuredOmega - estimatedBias_.gyroscope();
+  Matrix3 H;
+  const Rot3 R = Rot3::Expmap(theta, H);
+  const Vector3 theta_plus = theta + H.inverse() * correctedOmega * dt;
+  const Vector3 vel_plus = vel + R.rotate(correctedAcc) * dt;
+  const Vector3 vel_avg = 0.5 * (vel + vel_plus);
+  const Vector3 pos_plus = pos + vel_avg * dt;
+
+  // Add those values to estimate and linearize around them
+  values.insert<Vector3>(T(k_ + 1), theta_plus);
+  values.insert<Vector3>(P(k_ + 1), pos_plus);
+  values.insert<Vector3>(V(k_ + 1), vel_plus);
+  auto linear_graph = graph.linearize(values);
+
+  // add previous posterior
+  for (const auto& conditional : *posterior_k_)
+    linear_graph->add(boost::static_pointer_cast<GaussianFactor>(conditional));
+
+  // eliminate all but biases
+  // TODO(frank): does not seem to eliminate in order I want. What gives?
+  Ordering keys = list_of(T(k_))(P(k_))(V(k_))(T(k_ + 1))(P(k_ + 1))(V(k_ + 1));
+  SharedBayesNet bayesNet =
+      linear_graph->eliminatePartialSequential(keys, EliminateQR).first;
+
+  // The Bayes net now contains P(zeta(k)|zeta(k+1),bias) P(zeta(k+1)|bias)
+  // We marginalize zeta(k) by removing the conditionals on zeta(k)
+  // TODO(frank): could use erase(begin, begin+3) if order above was correct
+  SharedBayesNet marginal = boost::make_shared<GaussianBayesNet>();
+  for (const auto& conditional : *bayesNet) {
+    Symbol symbol(conditional->front());
+    if (symbol.index() > k_) marginal->push_back(conditional);
+  }
+
+  return marginal;
+}
+
+void AggregateImuReadings::integrateMeasurement(const Vector3& measuredAcc,
+                                                const Vector3& measuredOmega,
+                                                double dt) {
+  typedef map<Key, Matrix> Terms;
+
+  // Handle first time differently
+  if (k_ == 0)
+    posterior_k_ = initPosterior(measuredAcc, measuredOmega, dt);
+  else
+    posterior_k_ = integrateCorrected(measuredAcc, measuredOmega, dt);
+
+  // increment counter and time
+  k_ += 1;
+  deltaTij_ += dt;
+}
+
+NavState AggregateImuReadings::predict(const NavState& state_i,
+                                       const Bias& bias_i,
+                                       OptionalJacobian<9, 9> H1,
+                                       OptionalJacobian<9, 6> H2) const {
+  // TODO(frank): handle bias
+
+  // Get current estimates
+  Vector3 theta = values.at<Vector3>(T(k_));
+  Vector3 pos = values.at<Vector3>(P(k_));
+  Vector3 vel = values.at<Vector3>(V(k_));
+
+  // Correct for initial velocity and gravity
+  Rot3 Ri = state_i.attitude();
+  Matrix3 Rit = Ri.transpose();
+  Vector3 gt = deltaTij_ * p_->n_gravity;
+  pos += Rit * (state_i.velocity() * deltaTij_ + 0.5 * deltaTij_ * gt);
+  vel += Rit * gt;
+
+  // Convert local coordinates to manifold near state_i
+  Vector9 zeta;
+  zeta << theta, pos, vel;
+  return state_i.retract(zeta);
+}
+
+SharedGaussian AggregateImuReadings::noiseModel() const {
+  Matrix RS;
+  Vector d;
+  boost::tie(RS, d) = posterior_k_->matrix();
+
+  // R'*R = A'*A = inv(Cov)
+  // TODO(frank): think of a faster way - implement in noiseModel
+  return noiseModel::Gaussian::SqrtInformation(RS.block<9, 9>(0, 0), false);
+}
+
+Matrix9 AggregateImuReadings::preintMeasCov() const {
+  return noiseModel()->covariance();
+}
+
+}  // namespace gtsam

gtsam/navigation/AggregateImuReadings.h

@@ -0,0 +1,120 @@
+/* ----------------------------------------------------------------------------
+
+ * 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    AggregateImuReadings.h
+ * @brief   Integrates IMU readings on the NavState tangent space
+ * @author  Frank Dellaert
+ */
+
+#pragma once
+
+#include <gtsam/navigation/ImuFactor.h>
+#include <gtsam/linear/NoiseModel.h>
+
+namespace gtsam {
+
+class NonlinearFactorGraph;
+template <typename T>
+class Expression;
+typedef Expression<Vector3> Vector3_;
+
+// Convert covariance to diagonal noise model, if possible, otherwise throw
+static noiseModel::Diagonal::shared_ptr Diagonal(const Matrix& covariance) {
+  bool smart = true;
+  auto model = noiseModel::Gaussian::Covariance(covariance, smart);
+  auto diagonal = boost::dynamic_pointer_cast<noiseModel::Diagonal>(model);
+  if (!diagonal)
+    throw std::invalid_argument("ScenarioRunner::Diagonal: not a diagonal");
+  return diagonal;
+}
+
+class GaussianBayesNet;
+
+/**
+ * Class that integrates state estimate on the manifold.
+ * We integrate zeta = [theta, position, velocity]
+ * See ImuFactor.lyx for the relevant math.
+ */
+class AggregateImuReadings {
+ public:
+  typedef imuBias::ConstantBias Bias;
+  typedef ImuFactor::PreintegratedMeasurements::Params Params;
+  typedef boost::shared_ptr<GaussianBayesNet> SharedBayesNet;
+
+ private:
+  const boost::shared_ptr<Params> p_;
+  const SharedDiagonal accelerometerNoiseModel_, gyroscopeNoiseModel_;
+  const Bias estimatedBias_;
+
+  size_t k_;         ///< index/count of measurements integrated
+  double deltaTij_;  ///< sum of time increments
+
+  /// posterior on current iterate, stored as a Bayes net
+  /// P(delta_zeta|estimatedBias_delta):
+  SharedBayesNet posterior_k_;
+
+  /// Current estimate of zeta_k
+  Values values;
+
+ public:
+  AggregateImuReadings(const boost::shared_ptr<Params>& p,
+                       const Bias& estimatedBias = Bias())
+      : p_(p),
+        accelerometerNoiseModel_(Diagonal(p->accelerometerCovariance)),
+        gyroscopeNoiseModel_(Diagonal(p->gyroscopeCovariance)),
+        estimatedBias_(estimatedBias),
+        k_(0),
+        deltaTij_(0.0) {}
+
+  // We obtain discrete-time noise models by dividing the continuous-time
+  // covariances by dt:
+
+  SharedDiagonal discreteAccelerometerNoiseModel(double dt) const;
+  SharedDiagonal discreteGyroscopeNoiseModel(double dt) const;
+
+  /**
+   * Add a single IMU measurement to the preintegration.
+   * @param measuredAcc Measured acceleration (in body frame)
+   * @param measuredOmega Measured angular velocity (in body frame)
+   * @param dt Time interval between this and the last IMU measurement
+   */
+  void integrateMeasurement(const Vector3& measuredAcc,
+                            const Vector3& measuredOmega, double dt);
+
+  /// Predict state at time j
+  NavState predict(const NavState& state_i, const Bias& estimatedBias_i,
+                   OptionalJacobian<9, 9> H1 = boost::none,
+                   OptionalJacobian<9, 6> H2 = boost::none) const;
+
+  /// Return Gaussian noise model on prediction
+  SharedGaussian noiseModel() const;
+
+  /// @deprecated: Explicitly calculate covariance
+  Matrix9 preintMeasCov() const;
+
+ private:
+  NonlinearFactorGraph createGraph(const Vector3_& theta_,
+                                   const Vector3_& pose_, const Vector3_& vel_,
+                                   const Vector3& measuredAcc,
+                                   const Vector3& measuredOmega,
+                                   double dt) const;
+
+  // initialize posterior with first (corrected) IMU measurement
+  SharedBayesNet initPosterior(const Vector3& measuredAcc,
+                               const Vector3& measuredOmega, double dt);
+
+  // integrate
+  SharedBayesNet integrateCorrected(const Vector3& measuredAcc,
+                                    const Vector3& measuredOmega, double dt);
+};
+
+}  // namespace gtsam

gtsam/navigation/ImuBias.h

@@ -78,19 +78,19 @@ public:
 
   /** Correct an accelerometer measurement using this bias model, and optionally compute Jacobians */
   Vector3 correctAccelerometer(const Vector3& measurement,
-      OptionalJacobian<3, 6> H = boost::none) const {
+                               OptionalJacobian<3, 6> H1 = boost::none,
-    if (H) {
+                               OptionalJacobian<3, 3> H2 = boost::none) const {
-      (*H) << -I_3x3, Z_3x3;
+    if (H1) (*H1) << -I_3x3, Z_3x3;
-    }
+    if (H2) (*H2) << I_3x3;
     return measurement - biasAcc_;
   }
 
   /** Correct a gyroscope measurement using this bias model, and optionally compute Jacobians */
   Vector3 correctGyroscope(const Vector3& measurement,
-      OptionalJacobian<3, 6> H = boost::none) const {
+                           OptionalJacobian<3, 6> H1 = boost::none,
-    if (H) {
+                           OptionalJacobian<3, 3> H2 = boost::none) const {
-      (*H) << Z_3x3, -I_3x3;
+    if (H1) (*H1) << Z_3x3, -I_3x3;
-    }
+    if (H2) (*H2) << I_3x3;
     return measurement - biasGyro_;
   }
 

gtsam/navigation/ScenarioRunner.cpp

@@ -16,12 +16,6 @@
  */
 
 #include <gtsam/navigation/ScenarioRunner.h>
-#include <gtsam/linear/GaussianBayesNet.h>
-#include <gtsam/linear/GaussianFactorGraph.h>
-#include <gtsam/inference/Symbol.h>
-
-#include <boost/assign/std/list.hpp>
-
 #include <cmath>
 
 using namespace std;
@@ -29,194 +23,13 @@ using namespace boost::assign;
 
 namespace gtsam {
 
-using symbol_shorthand::T;  // for theta
-using symbol_shorthand::P;  // for position
-using symbol_shorthand::V;  // for velocity
-
-static const Symbol kBiasKey('B', 0);
-static const Matrix36 acc_H_bias = (Matrix36() << I_3x3, Z_3x3).finished();
-static const Matrix36 omega_H_bias = (Matrix36() << Z_3x3, I_3x3).finished();
-
-Vector9 PreintegratedMeasurements2::currentEstimate() const {
-  VectorValues biasValues;
-  biasValues.insert(kBiasKey, estimatedBias_.vector());
-  VectorValues zetaValues = posterior_k_->optimize(biasValues);
-  Vector9 zeta;
-  zeta << zetaValues.at(T(k_)), zetaValues.at(P(k_)), zetaValues.at(V(k_));
-  return zeta;
-}
-
-Vector3 PreintegratedMeasurements2::currentTheta() const {
-  // TODO(frank): make faster version theta = inv(R)*d
-  VectorValues biasValues;
-  biasValues.insert(kBiasKey, estimatedBias_.vector());
-  VectorValues zetaValues = posterior_k_->optimize(biasValues);
-  return zetaValues.at(T(k_));
-}
-
-SharedDiagonal PreintegratedMeasurements2::discreteAccelerometerNoiseModel(
-    double dt) const {
-  return noiseModel::Diagonal::Sigmas(accelerometerNoiseModel_->sigmas() /
-                                      std::sqrt(dt));
-}
-
-SharedDiagonal PreintegratedMeasurements2::discreteGyroscopeNoiseModel(
-    double dt) const {
-  return noiseModel::Diagonal::Sigmas(gyroscopeNoiseModel_->sigmas() /
-                                      std::sqrt(dt));
-}
-
-PreintegratedMeasurements2::SharedBayesNet
-PreintegratedMeasurements2::initPosterior(const Vector3& correctedAcc,
-                                          const Vector3& correctedOmega,
-                                          double dt) const {
-  typedef map<Key, Matrix> Terms;
-
-  // We create a factor graph and then compute P(zeta|bias)
-  GaussianFactorGraph graph;
-
-  // theta(1) = (correctedOmega - bias_delta) * dt
-  // => theta(1)/dt + bias_delta = correctedOmega
-  auto I_dt = I_3x3 / dt;
-  graph.add<Terms>({{T(k_ + 1), I_dt}, {kBiasKey, omega_H_bias}},
-                   correctedOmega, discreteGyroscopeNoiseModel(dt));
-
-  // pose(1) = (correctedAcc - bias_delta) * dt22
-  // => pose(1) / dt22 + bias_delta = correctedAcc
-  auto accModel = discreteAccelerometerNoiseModel(dt);
-  graph.add<Terms>({{P(k_ + 1), I_dt * (2.0 / dt)}, {kBiasKey, acc_H_bias}},
-                   correctedAcc, accModel);
-
-  // vel(1) = (correctedAcc - bias_delta) * dt
-  // => vel(1)/dt + bias_delta = correctedAcc
-  graph.add<Terms>({{V(k_ + 1), I_dt}, {kBiasKey, acc_H_bias}}, correctedAcc,
-                   accModel);
-
-  // eliminate all but biases
-  // NOTE(frank): After this, posterior_k_ contains P(zeta(1)|bias)
-  Ordering keys = list_of(T(k_ + 1))(P(k_ + 1))(V(k_ + 1));
-  return graph.eliminatePartialSequential(keys, EliminateQR).first;
-}
-
-PreintegratedMeasurements2::SharedBayesNet
-PreintegratedMeasurements2::integrateCorrected(const Vector3& correctedAcc,
-                                               const Vector3& correctedOmega,
-                                               double dt) const {
-  typedef map<Key, Matrix> Terms;
-
-  GaussianFactorGraph graph;
-
-  // estimate current theta from posterior
-  Vector3 theta_k = currentTheta();
-  Rot3 Rk = Rot3::Expmap(theta_k);
-  Matrix3 Rkt = Rk.transpose();
-
-  // add previous posterior
-  for (const auto& conditional : *posterior_k_)
-    graph.add(boost::static_pointer_cast<GaussianFactor>(conditional));
-
-  // theta(k+1) = theta(k) + inverse(H)*(correctedOmega - bias_delta) dt
-  // =>  H*theta(k+1)/dt - H*theta(k)/dt + bias_delta = (measuredOmega - bias)
-  Matrix3 H = Rot3::ExpmapDerivative(theta_k) / dt;
-  graph.add<Terms>({{T(k_ + 1), H}, {T(k_), -H}, {kBiasKey, omega_H_bias}},
-                   correctedOmega, discreteGyroscopeNoiseModel(dt));
-
-  double dt22 = 0.5 * dt * dt;
-  // pos(k+1) = pos(k) + vel(k) dt + Rk*(correctedAcc - bias_delta) dt22
-  //  => Rkt*pos(k+1)/dt22 - Rkt*pos(k)/dt22 - Rkt*vel(k) dt/dt22 + bias_delta
-  //     = correctedAcc
-  auto accModel = discreteAccelerometerNoiseModel(dt);
-  graph.add<Terms>({{P(k_ + 1), Rkt / dt22},
-                    {P(k_), -Rkt / dt22},
-                    {V(k_), -Rkt * (2.0 / dt)},
-                    {kBiasKey, acc_H_bias}},
-                   correctedAcc, accModel);
-
-  // vel(k+1) = vel(k) + Rk*(correctedAcc - bias_delta) dt
-  // =>  Rkt*vel(k+1)/dt - Rkt*vel(k)/dt + bias_delta = correctedAcc
-  graph.add<Terms>(
-      {{V(k_ + 1), Rkt / dt}, {V(k_), -Rkt / dt}, {kBiasKey, acc_H_bias}},
-      correctedAcc, accModel);
-
-  // eliminate all but biases
-  // TODO(frank): does not seem to eliminate in order I want. What gives?
-  Ordering keys = list_of(T(k_))(P(k_))(V(k_))(T(k_ + 1))(P(k_ + 1))(V(k_ + 1));
-  SharedBayesNet bayesNet =
-      graph.eliminatePartialSequential(keys, EliminateQR).first;
-
-  // The Bayes net now contains P(zeta(k)|zeta(k+1),bias) P(zeta(k+1)|bias)
-  // We marginalize zeta(k) by removing the conditionals on zeta(k)
-  // TODO(frank): could use erase(begin, begin+3) if order above was correct
-  SharedBayesNet marginal = boost::make_shared<GaussianBayesNet>();
-  for (const auto& conditional : *bayesNet) {
-    Symbol symbol(conditional->front());
-    if (symbol.index() > k_) marginal->push_back(conditional);
-  }
-
-  return marginal;
-}
-
-void PreintegratedMeasurements2::integrateMeasurement(
-    const Vector3& measuredAcc, const Vector3& measuredOmega, double dt) {
-  typedef map<Key, Matrix> Terms;
-
-  // Correct measurements by subtracting bias
-  Vector3 correctedAcc = measuredAcc - estimatedBias_.accelerometer();
-  Vector3 correctedOmega = measuredOmega - estimatedBias_.gyroscope();
-
-  // Handle first time differently
-  if (k_ == 0)
-    posterior_k_ = initPosterior(correctedAcc, correctedOmega, dt);
-  else
-    posterior_k_ = integrateCorrected(correctedAcc, correctedOmega, dt);
-
-  // increment counter and time
-  k_ += 1;
-  deltaTij_ += dt;
-}
-
-NavState PreintegratedMeasurements2::predict(
-    const NavState& state_i, const imuBias::ConstantBias& bias_i,
-    OptionalJacobian<9, 9> H1, OptionalJacobian<9, 6> H2) const {
-  // Get mean of current posterior on zeta
-  // TODO(frank): handle bias
-  Vector9 zeta = currentEstimate();
-
-  // Correct for initial velocity and gravity
-  Rot3 Ri = state_i.attitude();
-  Matrix3 Rit = Ri.transpose();
-  Vector3 gt = deltaTij_ * p_->n_gravity;
-  zeta.segment<3>(3) +=
-      Rit * (state_i.velocity() * deltaTij_ + 0.5 * deltaTij_ * gt);
-  zeta.segment<3>(6) += Rit * gt;
-
-  // Convert local coordinates to manifold near state_i
-  return state_i.retract(zeta);
-}
-
-SharedGaussian PreintegratedMeasurements2::noiseModel() const {
-  Matrix RS;
-  Vector d;
-  boost::tie(RS, d) = posterior_k_->matrix();
-
-  // R'*R = A'*A = inv(Cov)
-  // TODO(frank): think of a faster way - implement in noiseModel
-  return noiseModel::Gaussian::SqrtInformation(RS.block<9, 9>(0, 0), false);
-}
-
-Matrix9 PreintegratedMeasurements2::preintMeasCov() const {
-  return noiseModel()->covariance();
-}
-
-////////////////////////////////////////////////////////////////////////////////////////////
-
 static double intNoiseVar = 0.0000001;
 static const Matrix3 kIntegrationErrorCovariance = intNoiseVar * I_3x3;
 
-PreintegratedMeasurements2 ScenarioRunner::integrate(
+AggregateImuReadings ScenarioRunner::integrate(double T,
-    double T, const imuBias::ConstantBias& estimatedBias,
+                                               const Bias& estimatedBias,
-    bool corrupted) const {
+                                               bool corrupted) const {
-  PreintegratedMeasurements2 pim(p_, estimatedBias);
+  AggregateImuReadings pim(p_, estimatedBias);
 
   const double dt = imuSampleTime();
   const size_t nrSteps = T / dt;
@@ -231,15 +44,14 @@ PreintegratedMeasurements2 ScenarioRunner::integrate(
   return pim;
 }
 
-NavState ScenarioRunner::predict(
+NavState ScenarioRunner::predict(const AggregateImuReadings& pim,
-    const PreintegratedMeasurements2& pim,
+                                 const Bias& estimatedBias) const {
-    const imuBias::ConstantBias& estimatedBias) const {
   const NavState state_i(scenario_->pose(0), scenario_->velocity_n(0));
   return pim.predict(state_i, estimatedBias);
 }
 
-Matrix9 ScenarioRunner::estimateCovariance(
+Matrix9 ScenarioRunner::estimateCovariance(double T, size_t N,
-    double T, size_t N, const imuBias::ConstantBias& estimatedBias) const {
+                                           const Bias& estimatedBias) const {
   // Get predict prediction from ground truth measurements
   NavState prediction = predict(integrate(T));
 

gtsam/navigation/ScenarioRunner.h

@@ -16,126 +16,38 @@
  */
 
 #pragma once
-#include <gtsam/navigation/ImuFactor.h>
+#include <gtsam/navigation/AggregateImuReadings.h>
 #include <gtsam/navigation/Scenario.h>
 #include <gtsam/linear/Sampler.h>
 
 namespace gtsam {
 
-// Convert covariance to diagonal noise model, if possible, otherwise throw
-static noiseModel::Diagonal::shared_ptr Diagonal(const Matrix& covariance) {
-  bool smart = true;
-  auto model = noiseModel::Gaussian::Covariance(covariance, smart);
-  auto diagonal = boost::dynamic_pointer_cast<noiseModel::Diagonal>(model);
-  if (!diagonal)
-    throw std::invalid_argument("ScenarioRunner::Diagonal: not a diagonal");
-  return diagonal;
-}
-
-class GaussianBayesNet;
-
-/**
- * Class that integrates state estimate on the manifold.
- * We integrate zeta = [theta, position, velocity]
- * See ImuFactor.lyx for the relevant math.
- */
-class PreintegratedMeasurements2 {
- public:
-  typedef ImuFactor::PreintegratedMeasurements::Params Params;
-  typedef boost::shared_ptr<GaussianBayesNet> SharedBayesNet;
-
- private:
-  const boost::shared_ptr<Params> p_;
-  const SharedDiagonal accelerometerNoiseModel_, gyroscopeNoiseModel_;
-  const imuBias::ConstantBias estimatedBias_;
-
-  size_t k_;         ///< index/count of measurements integrated
-  double deltaTij_;  ///< sum of time increments
-
-  /// posterior on current iterate, stored as a Bayes net P(zeta|bias_delta):
-  SharedBayesNet posterior_k_;
-
- public:
-  PreintegratedMeasurements2(
-      const boost::shared_ptr<Params>& p,
-      const imuBias::ConstantBias& estimatedBias = imuBias::ConstantBias())
-      : p_(p),
-        accelerometerNoiseModel_(Diagonal(p->accelerometerCovariance)),
-        gyroscopeNoiseModel_(Diagonal(p->gyroscopeCovariance)),
-        estimatedBias_(estimatedBias),
-        k_(0),
-        deltaTij_(0.0) {}
-
-  SharedDiagonal discreteAccelerometerNoiseModel(double dt) const;
-  SharedDiagonal discreteGyroscopeNoiseModel(double dt) const;
-
-  /**
-   * Add a single IMU measurement to the preintegration.
-   * @param measuredAcc Measured acceleration (in body frame)
-   * @param measuredOmega Measured angular velocity (in body frame)
-   * @param dt Time interval between this and the last IMU measurement
-   */
-  void integrateMeasurement(const Vector3& measuredAcc,
-                            const Vector3& measuredOmega, double dt);
-
-  /// Predict state at time j
-  NavState predict(const NavState& state_i, const imuBias::ConstantBias& bias_i,
-                   OptionalJacobian<9, 9> H1 = boost::none,
-                   OptionalJacobian<9, 6> H2 = boost::none) const;
-
-  /// Return Gaussian noise model on prediction
-  SharedGaussian noiseModel() const;
-
-  /// @deprecated: Explicitly calculate covariance
-  Matrix9 preintMeasCov() const;
-
- private:
-  // estimate zeta given estimated biases
-  // calculates conditional mean of P(zeta|bias_delta)
-  Vector9 currentEstimate() const;
-
-  // estimate theta given estimated biases
-  Vector3 currentTheta() const;
-
-  // We obtain discrete-time noise models by dividing the continuous-time
-  // covariances by dt:
-
-  // initialize posterior with first (corrected) IMU measurement
-  SharedBayesNet initPosterior(const Vector3& correctedAcc,
-                               const Vector3& correctedOmega, double dt) const;
-
-  // integrate
-  SharedBayesNet integrateCorrected(const Vector3& correctedAcc,
-                                    const Vector3& correctedOmega,
-                                    double dt) const;
-};
-
 /*
  *  Simple class to test navigation scenarios.
  *  Takes a trajectory scenario as input, and can generate IMU measurements
  */
 class ScenarioRunner {
  public:
-  typedef boost::shared_ptr<PreintegratedMeasurements2::Params> SharedParams;
+  typedef imuBias::ConstantBias Bias;
+  typedef boost::shared_ptr<AggregateImuReadings::Params> SharedParams;
 
  private:
   const Scenario* scenario_;
   const SharedParams p_;
   const double imuSampleTime_, sqrt_dt_;
-  const imuBias::ConstantBias bias_;
+  const Bias estimatedBias_;
 
   // Create two samplers for acceleration and omega noise
   mutable Sampler gyroSampler_, accSampler_;
 
  public:
   ScenarioRunner(const Scenario* scenario, const SharedParams& p,
-                 double imuSampleTime = 1.0 / 100.0,
+                 double imuSampleTime = 1.0 / 100.0, const Bias& bias = Bias())
-                 const imuBias::ConstantBias& bias = imuBias::ConstantBias())
       : scenario_(scenario),
         p_(p),
         imuSampleTime_(imuSampleTime),
         sqrt_dt_(std::sqrt(imuSampleTime)),
-        bias_(bias),
+        estimatedBias_(bias),
         // NOTE(duy): random seeds that work well:
         gyroSampler_(Diagonal(p->gyroscopeCovariance), 10),
         accSampler_(Diagonal(p->accelerometerCovariance), 29284) {}
@@ -155,31 +67,27 @@ class ScenarioRunner {
 
   // versions corrupted by bias and noise
   Vector3 measured_omega_b(double t) const {
-    return actual_omega_b(t) + bias_.gyroscope() +
+    return actual_omega_b(t) + estimatedBias_.gyroscope() +
            gyroSampler_.sample() / sqrt_dt_;
   }
   Vector3 measured_specific_force_b(double t) const {
-    return actual_specific_force_b(t) + bias_.accelerometer() +
+    return actual_specific_force_b(t) + estimatedBias_.accelerometer() +
            accSampler_.sample() / sqrt_dt_;
   }
 
   const double& imuSampleTime() const { return imuSampleTime_; }
 
   /// Integrate measurements for T seconds into a PIM
-  PreintegratedMeasurements2 integrate(
+  AggregateImuReadings integrate(double T, const Bias& estimatedBias = Bias(),
-      double T,
+                                 bool corrupted = false) const;
-      const imuBias::ConstantBias& estimatedBias = imuBias::ConstantBias(),
-      bool corrupted = false) const;
 
   /// Predict predict given a PIM
-  NavState predict(const PreintegratedMeasurements2& pim,
+  NavState predict(const AggregateImuReadings& pim,
-                   const imuBias::ConstantBias& estimatedBias =
+                   const Bias& estimatedBias = Bias()) const;
-                       imuBias::ConstantBias()) const;
 
   /// Compute a Monte Carlo estimate of the predict covariance using N samples
   Matrix9 estimateCovariance(double T, size_t N = 1000,
-                             const imuBias::ConstantBias& estimatedBias =
+                             const Bias& estimatedBias = Bias()) const;
-                                 imuBias::ConstantBias()) const;
 
   /// Estimate covariance of sampled noise for sanity-check
   Matrix6 estimateNoiseCovariance(size_t N = 1000) const;

gtsam/navigation/functors.h

@@ -0,0 +1,154 @@
+/* ----------------------------------------------------------------------------
+
+ * 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    functors.h
+ * @brief   Functors for use in Navigation factors
+ * @author  Frank Dellaert
+ */
+
+#include <gtsam/geometry/Rot3.h>
+#include <gtsam/base/Matrix.h>
+#include <gtsam/base/OptionalJacobian.h>
+#include <cmath>
+
+namespace gtsam {
+
+// Implement Rot3::ExpmapDerivative(omega) * theta, with derivatives
+static Vector3 correctWithExpmapDerivative(
+    const Vector3& omega, const Vector3& theta,
+    OptionalJacobian<3, 3> H1 = boost::none,
+    OptionalJacobian<3, 3> H2 = boost::none) {
+  using std::sin;
+  const double angle2 = omega.dot(omega);  // rotation angle, squared
+  if (angle2 <= std::numeric_limits<double>::epsilon()) {
+    if (H1) *H1 = 0.5 * skewSymmetric(theta);
+    if (H2) *H2 = I_3x3;
+    return theta;
+  }
+
+  const double angle = std::sqrt(angle2);  // rotation angle
+  const double s1 = sin(angle) / angle;
+  const double s2 = sin(angle / 2.0);
+  const double a = 2.0 * s2 * s2 / angle2;
+  const double b = (1.0 - s1) / angle2;
+
+  const Vector3 omega_x_theta = omega.cross(theta);
+  const Vector3 yt = a * omega_x_theta;
+
+  const Matrix3 W = skewSymmetric(omega);
+  const Vector3 omega_x_omega_x_theta = omega.cross(omega_x_theta);
+  const Vector3 yyt = b * omega_x_omega_x_theta;
+
+  if (H1) {
+    Matrix13 omega_t = omega.transpose();
+    const Matrix3 T = skewSymmetric(theta);
+    const double Da = (s1 - 2.0 * a) / angle2;
+    const double Db = (3.0 * s1 - cos(angle) - 2.0) / angle2 / angle2;
+    *H1 = (-Da * omega_x_theta + Db * omega_x_omega_x_theta) * omega_t + a * T -
+          b * skewSymmetric(omega_x_theta) - b * W * T;
+  }
+  if (H2) *H2 = I_3x3 - a* W + b* W* W;
+
+  return theta - yt + yyt;
+}
+
+// theta(k+1) = theta(k) + inverse(H)*omega dt
+// omega = (H/dt_)*(theta(k+1) - H*theta(k))
+// TODO(frank): make linear expression
+class PredictAngularVelocity {
+ private:
+  double dt_;
+
+ public:
+  typedef Vector3 result_type;
+
+  PredictAngularVelocity(double dt) : dt_(dt) {}
+
+  Vector3 operator()(const Vector3& theta, const Vector3& theta_plus,
+                     OptionalJacobian<3, 3> H1 = boost::none,
+                     OptionalJacobian<3, 3> H2 = boost::none) const {
+    // TODO(frank): take into account derivative of ExpmapDerivative
+    const Vector3 predicted = (theta_plus - theta) / dt_;
+    Matrix3 D_c_t, D_c_p;
+    const Vector3 corrected =
+        correctWithExpmapDerivative(theta, predicted, D_c_t, D_c_p);
+    if (H1) *H1 = D_c_t - D_c_p / dt_;
+    if (H2) *H2 = D_c_p / dt_;
+    return corrected;
+  }
+};
+
+// TODO(frank): make linear expression
+static Vector3 averageVelocity(const Vector3& vel, const Vector3& vel_plus,
+                               OptionalJacobian<3, 3> H1 = boost::none,
+                               OptionalJacobian<3, 3> H2 = boost::none) {
+  // TODO(frank): take into account derivative of ExpmapDerivative
+  if (H1) *H1 = 0.5 * I_3x3;
+  if (H2) *H2 = 0.5 * I_3x3;
+  return 0.5 * (vel + vel_plus);
+}
+
+// pos(k+1) = pos(k) + average_velocity * dt
+// TODO(frank): make linear expression
+class PositionDefect {
+ private:
+  double dt_;
+
+ public:
+  typedef Vector3 result_type;
+
+  PositionDefect(double dt) : dt_(dt) {}
+
+  Vector3 operator()(const Vector3& pos, const Vector3& pos_plus,
+                     const Vector3& average_velocity,
+                     OptionalJacobian<3, 3> H1 = boost::none,
+                     OptionalJacobian<3, 3> H2 = boost::none,
+                     OptionalJacobian<3, 3> H3 = boost::none) const {
+    // TODO(frank): take into account derivative of ExpmapDerivative
+    if (H1) *H1 = I_3x3;
+    if (H2) *H2 = -I_3x3;
+    if (H3) *H3 = I_3x3* dt_;
+    return (pos + average_velocity * dt_) - pos_plus;
+  }
+};
+
+// vel(k+1) = vel(k) + Rk * acc * dt
+// acc = Rkt * [vel(k+1) - vel(k)]/dt
+// TODO(frank): take in Rot3
+class PredictAcceleration {
+ private:
+  double dt_;
+
+ public:
+  typedef Vector3 result_type;
+
+  PredictAcceleration(double dt) : dt_(dt) {}
+
+  Vector3 operator()(const Vector3& vel, const Vector3& vel_plus,
+                     const Vector3& theta,
+                     OptionalJacobian<3, 3> H1 = boost::none,
+                     OptionalJacobian<3, 3> H2 = boost::none,
+                     OptionalJacobian<3, 3> H3 = boost::none) const {
+    Matrix3 D_R_theta;
+    // TODO(frank): D_R_theta is ExpmapDerivative (computed again)
+    Rot3 nRb = Rot3::Expmap(theta, D_R_theta);
+    Vector3 n_acc = (vel_plus - vel) / dt_;
+    Matrix3 D_b_R, D_b_n;
+    Vector3 b_acc = nRb.unrotate(n_acc, D_b_R, D_b_n);
+    if (H1) *H1 = -D_b_n / dt_;
+    if (H2) *H2 = D_b_n / dt_;
+    if (H3) *H3 = D_b_R* D_R_theta;
+    return b_acc;
+  }
+};
+
+}  // namespace gtsam

gtsam/navigation/tests/testAggregateImuReadings.cpp

@@ -0,0 +1,165 @@
+/* ----------------------------------------------------------------------------
+
+ * 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    testInertialNavFactor.cpp
+ * @brief   Unit test for the InertialNavFactor
+ * @author  Frank Dellaert
+ */
+
+#include <gtsam/navigation/functors.h>
+#include <gtsam/navigation/AggregateImuReadings.h>
+#include <gtsam/base/numericalDerivative.h>
+
+#include <CppUnitLite/TestHarness.h>
+#include <boost/bind.hpp>
+
+using namespace std;
+using namespace gtsam;
+
+static const double kDt = 0.1;
+
+/* ************************************************************************* */
+TEST(AggregateImuReadings, CorrectWithExpmapDerivative1) {
+  Matrix aH1, aH2;
+  boost::function<Vector3(const Vector3&, const Vector3&)> f = boost::bind(
+      correctWithExpmapDerivative, _1, _2, boost::none, boost::none);
+  for (Vector3 omega : {Vector3(1, 0, 0), Vector3(0, 1, 0), Vector3(0, 0, 1)}) {
+    for (Vector3 theta :
+         {Vector3(1, 0, 0), Vector3(0, 1, 0), Vector3(0, 0, 1)}) {
+      Vector3 expected = Rot3::ExpmapDerivative(omega) * theta;
+      EXPECT(assert_equal(expected, correctWithExpmapDerivative(omega, theta)));
+      EXPECT(assert_equal(expected,
+                          correctWithExpmapDerivative(omega, theta, aH1, aH2)));
+      EXPECT(assert_equal(numericalDerivative21(f, omega, theta), aH1));
+      EXPECT(assert_equal(numericalDerivative22(f, omega, theta), aH2));
+    }
+  }
+}
+
+/* ************************************************************************* */
+TEST(AggregateImuReadings, CorrectWithExpmapDerivative2) {
+  Matrix aH1, aH2;
+  boost::function<Vector3(const Vector3&, const Vector3&)> f = boost::bind(
+      correctWithExpmapDerivative, _1, _2, boost::none, boost::none);
+  const Vector3 omega(0, 0, 0);
+  for (Vector3 theta : {Vector3(1, 0, 0), Vector3(0, 1, 0), Vector3(0, 0, 1)}) {
+    Vector3 expected = Rot3::ExpmapDerivative(omega) * theta;
+    EXPECT(assert_equal(expected, correctWithExpmapDerivative(omega, theta)));
+    EXPECT(assert_equal(expected,
+                        correctWithExpmapDerivative(omega, theta, aH1, aH2)));
+    EXPECT(assert_equal(numericalDerivative21(f, omega, theta), aH1));
+    EXPECT(assert_equal(numericalDerivative22(f, omega, theta), aH2));
+  }
+}
+
+/* ************************************************************************* */
+TEST(AggregateImuReadings, CorrectWithExpmapDerivative3) {
+  Matrix aH1, aH2;
+  boost::function<Vector3(const Vector3&, const Vector3&)> f = boost::bind(
+      correctWithExpmapDerivative, _1, _2, boost::none, boost::none);
+  const Vector3 omega(0.1, 0.2, 0.3), theta(0.4, 0.3, 0.2);
+  Vector3 expected = Rot3::ExpmapDerivative(omega) * theta;
+  EXPECT(assert_equal(expected, correctWithExpmapDerivative(omega, theta)));
+  EXPECT(assert_equal(expected,
+                      correctWithExpmapDerivative(omega, theta, aH1, aH2)));
+  EXPECT(assert_equal(numericalDerivative21(f, omega, theta), aH1));
+  EXPECT(assert_equal(numericalDerivative22(f, omega, theta), aH2));
+}
+
+/* ************************************************************************* */
+TEST(AggregateImuReadings, PredictAngularVelocity1) {
+  Matrix aH1, aH2;
+  PredictAngularVelocity functor(kDt);
+  boost::function<Vector3(const Vector3&, const Vector3&)> f =
+      boost::bind(functor, _1, _2, boost::none, boost::none);
+  const Vector3 theta(0, 0, 0), theta_plus(0.4, 0.3, 0.2);
+  EXPECT(assert_equal(Vector3(4, 3, 2), functor(theta, theta_plus, aH1, aH2)));
+  EXPECT(assert_equal(numericalDerivative21(f, theta, theta_plus), aH1));
+  EXPECT(assert_equal(numericalDerivative22(f, theta, theta_plus), aH2));
+}
+
+/* ************************************************************************* */
+TEST(AggregateImuReadings, PredictAngularVelocity2) {
+  Matrix aH1, aH2;
+  PredictAngularVelocity functor(kDt);
+  boost::function<Vector3(const Vector3&, const Vector3&)> f =
+      boost::bind(functor, _1, _2, boost::none, boost::none);
+  const Vector3 theta(0.1, 0.2, 0.3), theta_plus(0.4, 0.3, 0.2);
+  EXPECT(
+      assert_equal(Vector3(Rot3::ExpmapDerivative(theta) * Vector3(3, 1, -1)),
+                   functor(theta, theta_plus, aH1, aH2), 1e-5));
+  EXPECT(assert_equal(numericalDerivative21(f, theta, theta_plus), aH1));
+  EXPECT(assert_equal(numericalDerivative22(f, theta, theta_plus), aH2));
+}
+
+/* ************************************************************************* */
+TEST(AggregateImuReadings, AverageVelocity) {
+  Matrix aH1, aH2;
+  boost::function<Vector3(const Vector3&, const Vector3&)> f =
+      boost::bind(averageVelocity, _1, _2, boost::none, boost::none);
+  const Vector3 v(1, 2, 3), v_plus(3, 2, 1);
+  EXPECT(assert_equal(Vector3(2, 2, 2), averageVelocity(v, v_plus, aH1, aH2)));
+  EXPECT(assert_equal(numericalDerivative21(f, v, v_plus), aH1));
+  EXPECT(assert_equal(numericalDerivative22(f, v, v_plus), aH2));
+}
+
+/* ************************************************************************* */
+TEST(AggregateImuReadings, PositionDefect) {
+  Matrix aH1, aH2, aH3;
+  PositionDefect functor(kDt);
+  boost::function<Vector3(const Vector3&, const Vector3&, const Vector3&)> f =
+      boost::bind(functor, _1, _2, _3, boost::none, boost::none, boost::none);
+  const Vector3 pos(1, 2, 3), pos_plus(2, 4, 6);
+  const Vector3 avg(10, 20, 30);
+  EXPECT(assert_equal(Vector3(0, 0, 0),
+                      functor(pos, pos_plus, avg, aH1, aH2, aH3)));
+  EXPECT(assert_equal(numericalDerivative31(f, pos, pos_plus, avg), aH1));
+  EXPECT(assert_equal(numericalDerivative32(f, pos, pos_plus, avg), aH2));
+  EXPECT(assert_equal(numericalDerivative33(f, pos, pos_plus, avg), aH3));
+}
+
+/* ************************************************************************* */
+TEST(AggregateImuReadings, PredictAcceleration1) {
+  Matrix aH1, aH2, aH3;
+  PredictAcceleration functor(kDt);
+  boost::function<Vector3(const Vector3&, const Vector3&, const Vector3&)> f =
+      boost::bind(functor, _1, _2, _3, boost::none, boost::none, boost::none);
+  const Vector3 vel(1, 2, 3), vel_plus(2, 4, 6);
+  const Vector3 theta(0, 0, 0);
+  EXPECT(assert_equal(Vector3(10, 20, 30),
+                      functor(vel, vel_plus, theta, aH1, aH2, aH3)));
+  EXPECT(assert_equal(numericalDerivative31(f, vel, vel_plus, theta), aH1));
+  EXPECT(assert_equal(numericalDerivative32(f, vel, vel_plus, theta), aH2));
+  EXPECT(assert_equal(numericalDerivative33(f, vel, vel_plus, theta), aH3));
+}
+
+/* ************************************************************************* */
+TEST(AggregateImuReadings, PredictAcceleration2) {
+  Matrix aH1, aH2, aH3;
+  PredictAcceleration functor(kDt);
+  boost::function<Vector3(const Vector3&, const Vector3&, const Vector3&)> f =
+      boost::bind(functor, _1, _2, _3, boost::none, boost::none, boost::none);
+  const Vector3 vel(1, 2, 3), vel_plus(2, 4, 6);
+  const Vector3 theta(0.1, 0.2, 0.3);
+  EXPECT(assert_equal(Vector3(10, 20, 30),
+                      functor(vel, vel_plus, theta, aH1, aH2, aH3)));
+  EXPECT(assert_equal(numericalDerivative31(f, vel, vel_plus, theta), aH1));
+  EXPECT(assert_equal(numericalDerivative32(f, vel, vel_plus, theta), aH2));
+  EXPECT(assert_equal(numericalDerivative33(f, vel, vel_plus, theta), aH3));
+}
+
+/* ************************************************************************* */
+int main() {
+  TestResult tr;
+  return TestRegistry::runAllTests(tr);
+}
+/* ************************************************************************* */

gtsam/navigation/tests/testImuBias.cpp

@@ -16,113 +16,135 @@
  */
 
 #include <gtsam/navigation/ImuBias.h>
+#include <gtsam/base/numericalDerivative.h>
+
 #include <CppUnitLite/TestHarness.h>
+#include <boost/bind.hpp>
 
 using namespace std;
 using namespace gtsam;
 
+typedef imuBias::ConstantBias Bias;
+
 Vector biasAcc1(Vector3(0.2, -0.1, 0));
 Vector biasGyro1(Vector3(0.1, -0.3, -0.2));
-imuBias::ConstantBias bias1(biasAcc1, biasGyro1);
+Bias bias1(biasAcc1, biasGyro1);
 
 Vector biasAcc2(Vector3(0.1, 0.2, 0.04));
 Vector biasGyro2(Vector3(-0.002, 0.005, 0.03));
-imuBias::ConstantBias bias2(biasAcc2, biasGyro2);
+Bias bias2(biasAcc2, biasGyro2);
 
 /* ************************************************************************* */
-TEST( ImuBias, Constructor) {
+TEST(ImuBias, Constructor) {
   // Default Constructor
-  imuBias::ConstantBias bias1;
+  Bias bias1;
 
   // Acc + Gyro Constructor
-  imuBias::ConstantBias bias2(biasAcc2, biasGyro2);
+  Bias bias2(biasAcc2, biasGyro2);
 
   // Copy Constructor
-  imuBias::ConstantBias bias3(bias2);
+  Bias bias3(bias2);
 }
 
 /* ************************************************************************* */
-TEST( ImuBias, inverse) {
+TEST(ImuBias, inverse) {
-  imuBias::ConstantBias biasActual = bias1.inverse();
+  Bias biasActual = bias1.inverse();
-  imuBias::ConstantBias biasExpected = imuBias::ConstantBias(-biasAcc1,
+  Bias biasExpected = Bias(-biasAcc1, -biasGyro1);
-      -biasGyro1);
   EXPECT(assert_equal(biasExpected, biasActual));
 }
 
 /* ************************************************************************* */
-TEST( ImuBias, compose) {
+TEST(ImuBias, compose) {
-  imuBias::ConstantBias biasActual = bias2.compose(bias1);
+  Bias biasActual = bias2.compose(bias1);
-  imuBias::ConstantBias biasExpected = imuBias::ConstantBias(
+  Bias biasExpected = Bias(biasAcc1 + biasAcc2, biasGyro1 + biasGyro2);
-      biasAcc1 + biasAcc2, biasGyro1 + biasGyro2);
   EXPECT(assert_equal(biasExpected, biasActual));
 }
 
 /* ************************************************************************* */
-TEST( ImuBias, between) {
+TEST(ImuBias, between) {
   // p.between(q) == q - p
-  imuBias::ConstantBias biasActual = bias2.between(bias1);
+  Bias biasActual = bias2.between(bias1);
-  imuBias::ConstantBias biasExpected = imuBias::ConstantBias(
+  Bias biasExpected = Bias(biasAcc1 - biasAcc2, biasGyro1 - biasGyro2);
-      biasAcc1 - biasAcc2, biasGyro1 - biasGyro2);
   EXPECT(assert_equal(biasExpected, biasActual));
 }
 
 /* ************************************************************************* */
-TEST( ImuBias, localCoordinates) {
+TEST(ImuBias, localCoordinates) {
   Vector deltaActual = Vector(bias2.localCoordinates(bias1));
-  Vector deltaExpected = (imuBias::ConstantBias(biasAcc1 - biasAcc2,
+  Vector deltaExpected =
-      biasGyro1 - biasGyro2)).vector();
+      (Bias(biasAcc1 - biasAcc2, biasGyro1 - biasGyro2)).vector();
   EXPECT(assert_equal(deltaExpected, deltaActual));
 }
 
 /* ************************************************************************* */
-TEST( ImuBias, retract) {
+TEST(ImuBias, retract) {
   Vector6 delta;
   delta << 0.1, 0.2, -0.3, 0.1, -0.1, 0.2;
-  imuBias::ConstantBias biasActual = bias2.retract(delta);
+  Bias biasActual = bias2.retract(delta);
-  imuBias::ConstantBias biasExpected = imuBias::ConstantBias(
+  Bias biasExpected = Bias(biasAcc2 + delta.block<3, 1>(0, 0),
-      biasAcc2 + delta.block<3, 1>(0, 0), biasGyro2 + delta.block<3, 1>(3, 0));
+                           biasGyro2 + delta.block<3, 1>(3, 0));
   EXPECT(assert_equal(biasExpected, biasActual));
 }
 
 /* ************************************************************************* */
-TEST( ImuBias, Logmap) {
+TEST(ImuBias, Logmap) {
   Vector deltaActual = bias2.Logmap(bias1);
   Vector deltaExpected = bias1.vector();
   EXPECT(assert_equal(deltaExpected, deltaActual));
 }
 
 /* ************************************************************************* */
-TEST( ImuBias, Expmap) {
+TEST(ImuBias, Expmap) {
   Vector6 delta;
   delta << 0.1, 0.2, -0.3, 0.1, -0.1, 0.2;
-  imuBias::ConstantBias biasActual = bias2.Expmap(delta);
+  Bias biasActual = bias2.Expmap(delta);
-  imuBias::ConstantBias biasExpected = imuBias::ConstantBias(delta);
+  Bias biasExpected = Bias(delta);
   EXPECT(assert_equal(biasExpected, biasActual));
 }
 
 /* ************************************************************************* */
-TEST( ImuBias, operatorSub) {
+TEST(ImuBias, operatorSub) {
-  imuBias::ConstantBias biasActual = -bias1;
+  Bias biasActual = -bias1;
-  imuBias::ConstantBias biasExpected(-biasAcc1, -biasGyro1);
+  Bias biasExpected(-biasAcc1, -biasGyro1);
   EXPECT(assert_equal(biasExpected, biasActual));
 }
 
 /* ************************************************************************* */
-TEST( ImuBias, operatorAdd) {
+TEST(ImuBias, operatorAdd) {
-  imuBias::ConstantBias biasActual = bias2 + bias1;
+  Bias biasActual = bias2 + bias1;
-  imuBias::ConstantBias biasExpected(biasAcc2 + biasAcc1,
+  Bias biasExpected(biasAcc2 + biasAcc1, biasGyro2 + biasGyro1);
-      biasGyro2 + biasGyro1);
   EXPECT(assert_equal(biasExpected, biasActual));
 }
 
 /* ************************************************************************* */
-TEST( ImuBias, operatorSubB) {
+TEST(ImuBias, operatorSubB) {
-  imuBias::ConstantBias biasActual = bias2 - bias1;
+  Bias biasActual = bias2 - bias1;
-  imuBias::ConstantBias biasExpected(biasAcc2 - biasAcc1,
+  Bias biasExpected(biasAcc2 - biasAcc1, biasGyro2 - biasGyro1);
-      biasGyro2 - biasGyro1);
   EXPECT(assert_equal(biasExpected, biasActual));
 }
 
+/* ************************************************************************* */
+TEST(ImuBias, Correct1) {
+  Matrix aH1, aH2;
+  const Vector3 measurement(1, 2, 3);
+  boost::function<Vector3(const Bias&, const Vector3&)> f = boost::bind(
+      &Bias::correctAccelerometer, _1, _2, boost::none, boost::none);
+  bias1.correctAccelerometer(measurement, aH1, aH2);
+  EXPECT(assert_equal(numericalDerivative21(f, bias1, measurement), aH1));
+  EXPECT(assert_equal(numericalDerivative22(f, bias1, measurement), aH2));
+}
+
+/* ************************************************************************* */
+TEST(ImuBias, Correct2) {
+  Matrix aH1, aH2;
+  const Vector3 measurement(1, 2, 3);
+  boost::function<Vector3(const Bias&, const Vector3&)> f =
+      boost::bind(&Bias::correctGyroscope, _1, _2, boost::none, boost::none);
+  bias1.correctGyroscope(measurement, aH1, aH2);
+  EXPECT(assert_equal(numericalDerivative21(f, bias1, measurement), aH1));
+  EXPECT(assert_equal(numericalDerivative22(f, bias1, measurement), aH2));
+}
+
 /* ************************************************************************* */
 int main() {
   TestResult tr;

gtsam/navigation/tests/testScenarioRunner.cpp

@@ -31,7 +31,7 @@ static const Vector3 kAccBias(0.2, 0, 0), kRotBias(0.1, 0, 0.3);
 static const imuBias::ConstantBias kNonZeroBias(kAccBias, kRotBias);
 
 // Create default parameters with Z-down and above noise parameters
-static boost::shared_ptr<PreintegratedMeasurements2::Params> defaultParams() {
+static boost::shared_ptr<AggregateImuReadings::Params> defaultParams() {
   auto p = PreintegratedImuMeasurements::Params::MakeSharedD(10);
   p->gyroscopeCovariance = kGyroSigma * kGyroSigma * I_3x3;
   p->accelerometerCovariance = kAccelSigma * kAccelSigma * I_3x3;