| #include "frc971/vision/extrinsics_calibration.h" |
| |
| #include "aos/time/time.h" |
| #include "ceres/ceres.h" |
| #include "frc971/analysis/in_process_plotter.h" |
| #include "frc971/control_loops/runge_kutta.h" |
| #include "frc971/vision/calibration_accumulator.h" |
| #include "frc971/vision/charuco_lib.h" |
| |
| namespace frc971 { |
| namespace vision { |
| |
| namespace chrono = std::chrono; |
| using aos::distributed_clock; |
| using aos::monotonic_clock; |
| |
| constexpr double kGravity = 9.8; |
| |
| // The basic ideas here are taken from Kalibr. |
| // (https://github.com/ethz-asl/kalibr), but adapted to work with AOS, and to be |
| // simpler. |
| // |
| // Camera readings and IMU readings come in at different times, on different |
| // time scales. Our first problem is to align them in time so we can actually |
| // compute an error. This is done in the calibration accumulator code. The |
| // kalibr paper uses splines, while this uses kalman filters to solve the same |
| // interpolation problem so we can get the expected vs actual pose at the time |
| // each image arrives. |
| // |
| // The cost function is then fed the computed angular and positional error for |
| // each camera sample before the kalman filter update. Intuitively, the smaller |
| // the corrections to the kalman filter each step, the better the estimate |
| // should be. |
| // |
| // We don't actually implement the angular kalman filter because the IMU is so |
| // good. We give the solver an initial position and bias, and let it solve from |
| // there. This lets us represent drift that is linear in time, which should be |
| // good enough for ~1 minute calibration. |
| // |
| // TODO(austin): Kalman smoother ala |
| // https://stanford.edu/~boyd/papers/pdf/auto_ks.pdf should allow for better |
| // parallelism, and since we aren't causal, will take that into account a lot |
| // better. |
| |
| // This class takes the initial parameters and biases, and computes the error |
| // between the measured and expected camera readings. When optimized, this |
| // gives us a cost function to minimize. |
| template <typename Scalar> |
| class CeresPoseFilter : public CalibrationDataObserver { |
| public: |
| typedef Eigen::Transform<Scalar, 3, Eigen::Affine> Affine3s; |
| |
| CeresPoseFilter(Eigen::Quaternion<Scalar> initial_orientation, |
| Eigen::Quaternion<Scalar> imu_to_camera, |
| Eigen::Matrix<Scalar, 3, 1> gyro_bias, |
| Eigen::Matrix<Scalar, 6, 1> initial_state, |
| Eigen::Quaternion<Scalar> board_to_world, |
| Eigen::Matrix<Scalar, 3, 1> imu_to_camera_translation, |
| Scalar gravity_scalar, |
| Eigen::Matrix<Scalar, 3, 1> accelerometer_bias) |
| : accel_(Eigen::Matrix<double, 3, 1>::Zero()), |
| omega_(Eigen::Matrix<double, 3, 1>::Zero()), |
| imu_bias_(gyro_bias), |
| orientation_(initial_orientation), |
| x_hat_(initial_state), |
| p_(Eigen::Matrix<Scalar, 6, 6>::Zero()), |
| imu_to_camera_rotation_(imu_to_camera), |
| imu_to_camera_translation_(imu_to_camera_translation), |
| board_to_world_(board_to_world), |
| gravity_scalar_(gravity_scalar), |
| accelerometer_bias_(accelerometer_bias) {} |
| |
| Scalar gravity_scalar() { return gravity_scalar_; } |
| |
| virtual void ObserveCameraUpdate( |
| distributed_clock::time_point /*t*/, |
| Eigen::Vector3d /*board_to_camera_rotation*/, |
| Eigen::Quaternion<Scalar> /*imu_to_world_rotation*/, |
| Affine3s /*imu_to_world*/) {} |
| |
| // Observes a camera measurement by applying a kalman filter correction and |
| // accumulating up the error associated with the step. |
| void UpdateCamera(distributed_clock::time_point t, |
| std::pair<Eigen::Vector3d, Eigen::Vector3d> rt) override { |
| Integrate(t); |
| |
| const Eigen::Quaternion<Scalar> board_to_camera_rotation( |
| frc971::controls::ToQuaternionFromRotationVector(rt.first) |
| .cast<Scalar>()); |
| const Affine3s board_to_camera = |
| Eigen::Translation3d(rt.second).cast<Scalar>() * |
| board_to_camera_rotation; |
| |
| const Affine3s imu_to_camera = |
| imu_to_camera_translation_ * imu_to_camera_rotation_; |
| |
| // This converts us from (facing the board), |
| // x right, y up, z towards us -> x right, y away, z up. |
| // Confirmed to be right. |
| |
| // Want world -> imu rotation. |
| // world <- board <- camera <- imu. |
| const Eigen::Quaternion<Scalar> imu_to_world_rotation = |
| board_to_world_ * board_to_camera_rotation.inverse() * |
| imu_to_camera_rotation_; |
| |
| const Affine3s imu_to_world = |
| board_to_world_ * board_to_camera.inverse() * imu_to_camera; |
| |
| const Eigen::Matrix<Scalar, 3, 1> z = |
| imu_to_world * Eigen::Matrix<Scalar, 3, 1>::Zero(); |
| |
| Eigen::Matrix<Scalar, 3, 6> H = Eigen::Matrix<Scalar, 3, 6>::Zero(); |
| H(0, 0) = static_cast<Scalar>(1.0); |
| H(1, 1) = static_cast<Scalar>(1.0); |
| H(2, 2) = static_cast<Scalar>(1.0); |
| const Eigen::Matrix<Scalar, 3, 1> y = z - H * x_hat_; |
| |
| const Eigen::Matrix<double, 3, 3> R = |
| (::Eigen::DiagonalMatrix<double, 3>().diagonal() << ::std::pow(0.01, 2), |
| ::std::pow(0.01, 2), ::std::pow(0.01, 2)) |
| .finished() |
| .asDiagonal(); |
| |
| const Eigen::Matrix<Scalar, 3, 3> S = |
| H * p_ * H.transpose() + R.cast<Scalar>(); |
| const Eigen::Matrix<Scalar, 6, 3> K = p_ * H.transpose() * S.inverse(); |
| |
| x_hat_ += K * y; |
| p_ = (Eigen::Matrix<Scalar, 6, 6>::Identity() - K * H) * p_; |
| |
| const Eigen::Quaternion<Scalar> error(imu_to_world_rotation.inverse() * |
| orientation()); |
| |
| errors_.emplace_back( |
| Eigen::Matrix<Scalar, 3, 1>(error.x(), error.y(), error.z())); |
| position_errors_.emplace_back(y); |
| |
| ObserveCameraUpdate(t, rt.first, imu_to_world_rotation, imu_to_world); |
| } |
| |
| virtual void ObserveIMUUpdate( |
| distributed_clock::time_point /*t*/, |
| std::pair<Eigen::Vector3d, Eigen::Vector3d> /*wa*/) {} |
| |
| void UpdateIMU(distributed_clock::time_point t, |
| std::pair<Eigen::Vector3d, Eigen::Vector3d> wa) override { |
| Integrate(t); |
| omega_ = wa.first; |
| accel_ = wa.second; |
| |
| ObserveIMUUpdate(t, wa); |
| } |
| |
| const Eigen::Quaternion<Scalar> &orientation() const { return orientation_; } |
| |
| size_t num_errors() const { return errors_.size(); } |
| Scalar errorx(size_t i) const { return errors_[i].x(); } |
| Scalar errory(size_t i) const { return errors_[i].y(); } |
| Scalar errorz(size_t i) const { return errors_[i].z(); } |
| |
| size_t num_perrors() const { return position_errors_.size(); } |
| Scalar errorpx(size_t i) const { return position_errors_[i].x(); } |
| Scalar errorpy(size_t i) const { return position_errors_[i].y(); } |
| Scalar errorpz(size_t i) const { return position_errors_[i].z(); } |
| |
| private: |
| Eigen::Matrix<Scalar, 46, 1> Pack(Eigen::Quaternion<Scalar> q, |
| Eigen::Matrix<Scalar, 6, 1> x_hat, |
| Eigen::Matrix<Scalar, 6, 6> p) { |
| Eigen::Matrix<Scalar, 46, 1> result = Eigen::Matrix<Scalar, 46, 1>::Zero(); |
| result.template block<4, 1>(0, 0) = q.coeffs(); |
| result.template block<6, 1>(4, 0) = x_hat; |
| result.template block<36, 1>(10, 0) = |
| Eigen::Map<Eigen::Matrix<Scalar, 36, 1>>(p.data(), p.size()); |
| |
| return result; |
| } |
| |
| std::tuple<Eigen::Quaternion<Scalar>, Eigen::Matrix<Scalar, 6, 1>, |
| Eigen::Matrix<Scalar, 6, 6>> |
| UnPack(Eigen::Matrix<Scalar, 46, 1> input) { |
| Eigen::Quaternion<Scalar> q(input.template block<4, 1>(0, 0)); |
| Eigen::Matrix<Scalar, 6, 1> x_hat(input.template block<6, 1>(4, 0)); |
| Eigen::Matrix<Scalar, 6, 6> p = |
| Eigen::Map<Eigen::Matrix<Scalar, 6, 6>>(input.data() + 10, 6, 6); |
| return std::make_tuple(q, x_hat, p); |
| } |
| |
| Eigen::Matrix<Scalar, 46, 1> Derivative( |
| const Eigen::Matrix<Scalar, 46, 1> &input) { |
| auto [q, x_hat, p] = UnPack(input); |
| |
| Eigen::Quaternion<Scalar> omega_q; |
| omega_q.w() = Scalar(0.0); |
| omega_q.vec() = 0.5 * (omega_.cast<Scalar>() - imu_bias_); |
| Eigen::Matrix<Scalar, 4, 1> q_dot = (q * omega_q).coeffs(); |
| |
| Eigen::Matrix<double, 6, 6> A = Eigen::Matrix<double, 6, 6>::Zero(); |
| A(0, 3) = 1.0; |
| A(1, 4) = 1.0; |
| A(2, 5) = 1.0; |
| |
| Eigen::Matrix<Scalar, 6, 1> x_hat_dot = A * x_hat; |
| x_hat_dot.template block<3, 1>(3, 0) = |
| orientation() * (accel_.cast<Scalar>() - accelerometer_bias_) - |
| Eigen::Vector3d(0, 0, kGravity).cast<Scalar>() * gravity_scalar_; |
| |
| // Initialize the position noise to 0. If the solver is going to back-solve |
| // for the most likely starting position, let's just say that the noise is |
| // small. |
| constexpr double kPositionNoise = 0.0; |
| constexpr double kAccelerometerNoise = 2.3e-6 * 9.8; |
| constexpr double kIMUdt = 5.0e-4; |
| Eigen::Matrix<double, 6, 6> Q_dot( |
| (::Eigen::DiagonalMatrix<double, 6>().diagonal() |
| << ::std::pow(kPositionNoise, 2) / kIMUdt, |
| ::std::pow(kPositionNoise, 2) / kIMUdt, |
| ::std::pow(kPositionNoise, 2) / kIMUdt, |
| ::std::pow(kAccelerometerNoise, 2) / kIMUdt, |
| ::std::pow(kAccelerometerNoise, 2) / kIMUdt, |
| ::std::pow(kAccelerometerNoise, 2) / kIMUdt) |
| .finished() |
| .asDiagonal()); |
| Eigen::Matrix<Scalar, 6, 6> p_dot = A.cast<Scalar>() * p + |
| p * A.transpose().cast<Scalar>() + |
| Q_dot.cast<Scalar>(); |
| |
| return Pack(Eigen::Quaternion<Scalar>(q_dot), x_hat_dot, p_dot); |
| } |
| |
| virtual void ObserveIntegrated(distributed_clock::time_point /*t*/, |
| Eigen::Matrix<Scalar, 6, 1> /*x_hat*/, |
| Eigen::Quaternion<Scalar> /*orientation*/, |
| Eigen::Matrix<Scalar, 6, 6> /*p*/) {} |
| |
| void Integrate(distributed_clock::time_point t) { |
| if (last_time_ != distributed_clock::min_time) { |
| Eigen::Matrix<Scalar, 46, 1> next = control_loops::RungeKutta( |
| [this](auto r) { return Derivative(r); }, |
| Pack(orientation_, x_hat_, p_), |
| aos::time::DurationInSeconds(t - last_time_)); |
| |
| std::tie(orientation_, x_hat_, p_) = UnPack(next); |
| |
| // Normalize q so it doesn't drift. |
| orientation_.normalize(); |
| } |
| |
| last_time_ = t; |
| ObserveIntegrated(t, x_hat_, orientation_, p_); |
| } |
| |
| Eigen::Matrix<double, 3, 1> accel_; |
| Eigen::Matrix<double, 3, 1> omega_; |
| Eigen::Matrix<Scalar, 3, 1> imu_bias_; |
| |
| // IMU -> world quaternion |
| Eigen::Quaternion<Scalar> orientation_; |
| Eigen::Matrix<Scalar, 6, 1> x_hat_; |
| Eigen::Matrix<Scalar, 6, 6> p_; |
| distributed_clock::time_point last_time_ = distributed_clock::min_time; |
| |
| Eigen::Quaternion<Scalar> imu_to_camera_rotation_; |
| Eigen::Translation<Scalar, 3> imu_to_camera_translation_ = |
| Eigen::Translation3d(0, 0, 0).cast<Scalar>(); |
| |
| Eigen::Quaternion<Scalar> board_to_world_; |
| Scalar gravity_scalar_; |
| Eigen::Matrix<Scalar, 3, 1> accelerometer_bias_; |
| // States: |
| // xyz position |
| // xyz velocity |
| // |
| // Inputs |
| // xyz accel |
| // |
| // Measurement: |
| // xyz position from camera. |
| // |
| // Since the gyro is so good, we can just solve for the bias and initial |
| // position with the solver and see what it learns. |
| |
| // Returns the angular errors for each camera sample. |
| std::vector<Eigen::Matrix<Scalar, 3, 1>> errors_; |
| std::vector<Eigen::Matrix<Scalar, 3, 1>> position_errors_; |
| }; |
| |
| // Subclass of the filter above which has plotting. This keeps debug code and |
| // actual code separate. |
| class PoseFilter : public CeresPoseFilter<double> { |
| public: |
| PoseFilter(Eigen::Quaternion<double> initial_orientation, |
| Eigen::Quaternion<double> imu_to_camera, |
| Eigen::Matrix<double, 3, 1> gyro_bias, |
| Eigen::Matrix<double, 6, 1> initial_state, |
| Eigen::Quaternion<double> board_to_world, |
| Eigen::Matrix<double, 3, 1> imu_to_camera_translation, |
| double gravity_scalar, |
| Eigen::Matrix<double, 3, 1> accelerometer_bias) |
| : CeresPoseFilter<double>(initial_orientation, imu_to_camera, gyro_bias, |
| initial_state, board_to_world, |
| imu_to_camera_translation, gravity_scalar, |
| accelerometer_bias) {} |
| |
| void Plot() { |
| std::vector<double> rx; |
| std::vector<double> ry; |
| std::vector<double> rz; |
| std::vector<double> x; |
| std::vector<double> y; |
| std::vector<double> z; |
| std::vector<double> vx; |
| std::vector<double> vy; |
| std::vector<double> vz; |
| for (const Eigen::Quaternion<double> &q : orientations_) { |
| Eigen::Matrix<double, 3, 1> rotation_vector = |
| frc971::controls::ToRotationVectorFromQuaternion(q); |
| rx.emplace_back(rotation_vector(0, 0)); |
| ry.emplace_back(rotation_vector(1, 0)); |
| rz.emplace_back(rotation_vector(2, 0)); |
| } |
| for (const Eigen::Matrix<double, 6, 1> &x_hat : x_hats_) { |
| x.emplace_back(x_hat(0)); |
| y.emplace_back(x_hat(1)); |
| z.emplace_back(x_hat(2)); |
| vx.emplace_back(x_hat(3)); |
| vy.emplace_back(x_hat(4)); |
| vz.emplace_back(x_hat(5)); |
| } |
| |
| frc971::analysis::Plotter plotter; |
| plotter.AddFigure("position"); |
| plotter.AddLine(times_, rx, "x_hat(0)"); |
| plotter.AddLine(times_, ry, "x_hat(1)"); |
| plotter.AddLine(times_, rz, "x_hat(2)"); |
| plotter.AddLine(camera_times_, camera_x_, "Camera x"); |
| plotter.AddLine(camera_times_, camera_y_, "Camera y"); |
| plotter.AddLine(camera_times_, camera_z_, "Camera z"); |
| plotter.AddLine(camera_times_, camera_error_x_, "Camera error x"); |
| plotter.AddLine(camera_times_, camera_error_y_, "Camera error y"); |
| plotter.AddLine(camera_times_, camera_error_z_, "Camera error z"); |
| plotter.Publish(); |
| |
| plotter.AddFigure("error"); |
| plotter.AddLine(times_, rx, "x_hat(0)"); |
| plotter.AddLine(times_, ry, "x_hat(1)"); |
| plotter.AddLine(times_, rz, "x_hat(2)"); |
| plotter.AddLine(camera_times_, camera_error_x_, "Camera error x"); |
| plotter.AddLine(camera_times_, camera_error_y_, "Camera error y"); |
| plotter.AddLine(camera_times_, camera_error_z_, "Camera error z"); |
| plotter.Publish(); |
| |
| plotter.AddFigure("imu"); |
| plotter.AddLine(camera_times_, world_gravity_x_, "world_gravity(0)"); |
| plotter.AddLine(camera_times_, world_gravity_y_, "world_gravity(1)"); |
| plotter.AddLine(camera_times_, world_gravity_z_, "world_gravity(2)"); |
| plotter.AddLine(imu_times_, imu_x_, "imu x"); |
| plotter.AddLine(imu_times_, imu_y_, "imu y"); |
| plotter.AddLine(imu_times_, imu_z_, "imu z"); |
| plotter.AddLine(times_, rx, "rotation x"); |
| plotter.AddLine(times_, ry, "rotation y"); |
| plotter.AddLine(times_, rz, "rotation z"); |
| plotter.Publish(); |
| |
| plotter.AddFigure("raw"); |
| plotter.AddLine(imu_times_, imu_x_, "imu x"); |
| plotter.AddLine(imu_times_, imu_y_, "imu y"); |
| plotter.AddLine(imu_times_, imu_z_, "imu z"); |
| plotter.AddLine(imu_times_, imu_ratex_, "omega x"); |
| plotter.AddLine(imu_times_, imu_ratey_, "omega y"); |
| plotter.AddLine(imu_times_, imu_ratez_, "omega z"); |
| plotter.AddLine(camera_times_, raw_camera_x_, "Camera x"); |
| plotter.AddLine(camera_times_, raw_camera_y_, "Camera y"); |
| plotter.AddLine(camera_times_, raw_camera_z_, "Camera z"); |
| plotter.Publish(); |
| |
| plotter.AddFigure("xyz vel"); |
| plotter.AddLine(times_, x, "x"); |
| plotter.AddLine(times_, y, "y"); |
| plotter.AddLine(times_, z, "z"); |
| plotter.AddLine(times_, vx, "vx"); |
| plotter.AddLine(times_, vy, "vy"); |
| plotter.AddLine(times_, vz, "vz"); |
| plotter.AddLine(camera_times_, camera_position_x_, "Camera x"); |
| plotter.AddLine(camera_times_, camera_position_y_, "Camera y"); |
| plotter.AddLine(camera_times_, camera_position_z_, "Camera z"); |
| plotter.Publish(); |
| |
| plotter.Spin(); |
| } |
| |
| void ObserveIntegrated(distributed_clock::time_point t, |
| Eigen::Matrix<double, 6, 1> x_hat, |
| Eigen::Quaternion<double> orientation, |
| Eigen::Matrix<double, 6, 6> p) override { |
| VLOG(1) << t << " -> " << p; |
| VLOG(1) << t << " xhat -> " << x_hat.transpose(); |
| times_.emplace_back(chrono::duration<double>(t.time_since_epoch()).count()); |
| x_hats_.emplace_back(x_hat); |
| orientations_.emplace_back(orientation); |
| } |
| |
| void ObserveIMUUpdate( |
| distributed_clock::time_point t, |
| std::pair<Eigen::Vector3d, Eigen::Vector3d> wa) override { |
| imu_times_.emplace_back(chrono::duration<double>(t.time_since_epoch()).count()); |
| imu_ratex_.emplace_back(wa.first.x()); |
| imu_ratey_.emplace_back(wa.first.y()); |
| imu_ratez_.emplace_back(wa.first.z()); |
| imu_x_.emplace_back(wa.second.x()); |
| imu_y_.emplace_back(wa.second.y()); |
| imu_z_.emplace_back(wa.second.z()); |
| |
| last_accel_ = wa.second; |
| } |
| |
| void ObserveCameraUpdate(distributed_clock::time_point t, |
| Eigen::Vector3d board_to_camera_rotation, |
| Eigen::Quaternion<double> imu_to_world_rotation, |
| Eigen::Affine3d imu_to_world) override { |
| raw_camera_x_.emplace_back(board_to_camera_rotation(0, 0)); |
| raw_camera_y_.emplace_back(board_to_camera_rotation(1, 0)); |
| raw_camera_z_.emplace_back(board_to_camera_rotation(2, 0)); |
| |
| Eigen::Matrix<double, 3, 1> rotation_vector = |
| frc971::controls::ToRotationVectorFromQuaternion(imu_to_world_rotation); |
| camera_times_.emplace_back( |
| chrono::duration<double>(t.time_since_epoch()).count()); |
| |
| Eigen::Matrix<double, 3, 1> camera_error = |
| frc971::controls::ToRotationVectorFromQuaternion( |
| imu_to_world_rotation.inverse() * orientation()); |
| |
| camera_x_.emplace_back(rotation_vector(0, 0)); |
| camera_y_.emplace_back(rotation_vector(1, 0)); |
| camera_z_.emplace_back(rotation_vector(2, 0)); |
| |
| camera_error_x_.emplace_back(camera_error(0, 0)); |
| camera_error_y_.emplace_back(camera_error(1, 0)); |
| camera_error_z_.emplace_back(camera_error(2, 0)); |
| |
| const Eigen::Vector3d world_gravity = |
| imu_to_world_rotation * last_accel_ - |
| Eigen::Vector3d(0, 0, kGravity) * gravity_scalar(); |
| |
| const Eigen::Vector3d camera_position = |
| imu_to_world * Eigen::Vector3d::Zero(); |
| |
| world_gravity_x_.emplace_back(world_gravity.x()); |
| world_gravity_y_.emplace_back(world_gravity.y()); |
| world_gravity_z_.emplace_back(world_gravity.z()); |
| |
| camera_position_x_.emplace_back(camera_position.x()); |
| camera_position_y_.emplace_back(camera_position.y()); |
| camera_position_z_.emplace_back(camera_position.z()); |
| } |
| |
| std::vector<double> camera_times_; |
| std::vector<double> camera_x_; |
| std::vector<double> camera_y_; |
| std::vector<double> camera_z_; |
| std::vector<double> raw_camera_x_; |
| std::vector<double> raw_camera_y_; |
| std::vector<double> raw_camera_z_; |
| std::vector<double> camera_error_x_; |
| std::vector<double> camera_error_y_; |
| std::vector<double> camera_error_z_; |
| |
| std::vector<double> world_gravity_x_; |
| std::vector<double> world_gravity_y_; |
| std::vector<double> world_gravity_z_; |
| std::vector<double> imu_x_; |
| std::vector<double> imu_y_; |
| std::vector<double> imu_z_; |
| std::vector<double> camera_position_x_; |
| std::vector<double> camera_position_y_; |
| std::vector<double> camera_position_z_; |
| |
| std::vector<double> imu_times_; |
| std::vector<double> imu_ratex_; |
| std::vector<double> imu_ratey_; |
| std::vector<double> imu_ratez_; |
| |
| std::vector<double> times_; |
| std::vector<Eigen::Matrix<double, 6, 1>> x_hats_; |
| std::vector<Eigen::Quaternion<double>> orientations_; |
| |
| Eigen::Matrix<double, 3, 1> last_accel_ = Eigen::Matrix<double, 3, 1>::Zero(); |
| }; |
| |
| // Adapter class from the KF above to a Ceres cost function. |
| struct CostFunctor { |
| CostFunctor(const CalibrationData *d) : data(d) {} |
| |
| const CalibrationData *data; |
| |
| template <typename S> |
| bool operator()(const S *const q1, const S *const q2, const S *const v, |
| const S *const p, const S *const btw, const S *const itc, |
| const S *const gravity_scalar_ptr, |
| const S *const accelerometer_bias_ptr, S *residual) const { |
| Eigen::Quaternion<S> initial_orientation(q1[3], q1[0], q1[1], q1[2]); |
| Eigen::Quaternion<S> mounting_orientation(q2[3], q2[0], q2[1], q2[2]); |
| Eigen::Quaternion<S> board_to_world(btw[3], btw[0], btw[1], btw[2]); |
| Eigen::Matrix<S, 3, 1> gyro_bias(v[0], v[1], v[2]); |
| Eigen::Matrix<S, 6, 1> initial_state; |
| initial_state(0) = p[0]; |
| initial_state(1) = p[1]; |
| initial_state(2) = p[2]; |
| initial_state(3) = p[3]; |
| initial_state(4) = p[4]; |
| initial_state(5) = p[5]; |
| Eigen::Matrix<S, 3, 1> imu_to_camera_translation(itc[0], itc[1], itc[2]); |
| Eigen::Matrix<S, 3, 1> accelerometer_bias(accelerometer_bias_ptr[0], |
| accelerometer_bias_ptr[1], |
| accelerometer_bias_ptr[2]); |
| |
| CeresPoseFilter<S> filter(initial_orientation, mounting_orientation, |
| gyro_bias, initial_state, board_to_world, |
| imu_to_camera_translation, *gravity_scalar_ptr, |
| accelerometer_bias); |
| data->ReviewData(&filter); |
| |
| for (size_t i = 0; i < filter.num_errors(); ++i) { |
| residual[3 * i + 0] = filter.errorx(i); |
| residual[3 * i + 1] = filter.errory(i); |
| residual[3 * i + 2] = filter.errorz(i); |
| } |
| |
| for (size_t i = 0; i < filter.num_perrors(); ++i) { |
| residual[3 * filter.num_errors() + 3 * i + 0] = filter.errorpx(i); |
| residual[3 * filter.num_errors() + 3 * i + 1] = filter.errorpy(i); |
| residual[3 * filter.num_errors() + 3 * i + 2] = filter.errorpz(i); |
| } |
| |
| return true; |
| } |
| }; |
| |
| void Solve(const CalibrationData &data, |
| CalibrationParameters *calibration_parameters) { |
| ceres::Problem problem; |
| |
| ceres::EigenQuaternionParameterization *quaternion_local_parameterization = |
| new ceres::EigenQuaternionParameterization(); |
| // Set up the only cost function (also known as residual). This uses |
| // auto-differentiation to obtain the derivative (jacobian). |
| |
| ceres::CostFunction *cost_function = |
| new ceres::AutoDiffCostFunction<CostFunctor, ceres::DYNAMIC, 4, 4, 3, 6, |
| 4, 3, 1, 3>( |
| new CostFunctor(&data), data.camera_samples_size() * 6); |
| problem.AddResidualBlock( |
| cost_function, new ceres::HuberLoss(1.0), |
| calibration_parameters->initial_orientation.coeffs().data(), |
| calibration_parameters->imu_to_camera.coeffs().data(), |
| calibration_parameters->gyro_bias.data(), |
| calibration_parameters->initial_state.data(), |
| calibration_parameters->board_to_world.coeffs().data(), |
| calibration_parameters->imu_to_camera_translation.data(), |
| &calibration_parameters->gravity_scalar, |
| calibration_parameters->accelerometer_bias.data()); |
| problem.SetParameterization( |
| calibration_parameters->initial_orientation.coeffs().data(), |
| quaternion_local_parameterization); |
| problem.SetParameterization( |
| calibration_parameters->imu_to_camera.coeffs().data(), |
| quaternion_local_parameterization); |
| problem.SetParameterization( |
| calibration_parameters->board_to_world.coeffs().data(), |
| quaternion_local_parameterization); |
| for (int i = 0; i < 3; ++i) { |
| problem.SetParameterLowerBound(calibration_parameters->gyro_bias.data(), i, |
| -0.05); |
| problem.SetParameterUpperBound(calibration_parameters->gyro_bias.data(), i, |
| 0.05); |
| problem.SetParameterLowerBound( |
| calibration_parameters->accelerometer_bias.data(), i, -0.05); |
| problem.SetParameterUpperBound( |
| calibration_parameters->accelerometer_bias.data(), i, 0.05); |
| } |
| problem.SetParameterLowerBound(&calibration_parameters->gravity_scalar, 0, |
| 0.95); |
| problem.SetParameterUpperBound(&calibration_parameters->gravity_scalar, 0, |
| 1.05); |
| |
| // Run the solver! |
| ceres::Solver::Options options; |
| options.minimizer_progress_to_stdout = true; |
| options.gradient_tolerance = 1e-12; |
| options.function_tolerance = 1e-16; |
| options.parameter_tolerance = 1e-12; |
| ceres::Solver::Summary summary; |
| Solve(options, &problem, &summary); |
| LOG(INFO) << summary.FullReport(); |
| |
| LOG(INFO) << "initial_orientation " |
| << calibration_parameters->initial_orientation.coeffs().transpose(); |
| LOG(INFO) << "imu_to_camera " |
| << calibration_parameters->imu_to_camera.coeffs().transpose(); |
| LOG(INFO) << "imu_to_camera(rotation) " |
| << frc971::controls::ToRotationVectorFromQuaternion( |
| calibration_parameters->imu_to_camera) |
| .transpose(); |
| LOG(INFO) << "gyro_bias " << calibration_parameters->gyro_bias.transpose(); |
| LOG(INFO) << "board_to_world " |
| << calibration_parameters->board_to_world.coeffs().transpose(); |
| LOG(INFO) << "board_to_world(rotation) " |
| << frc971::controls::ToRotationVectorFromQuaternion( |
| calibration_parameters->board_to_world) |
| .transpose(); |
| LOG(INFO) << "imu_to_camera_translation " |
| << calibration_parameters->imu_to_camera_translation.transpose(); |
| LOG(INFO) << "gravity " << kGravity * calibration_parameters->gravity_scalar; |
| LOG(INFO) << "accelerometer bias " |
| << calibration_parameters->accelerometer_bias.transpose(); |
| } |
| |
| void Plot(const CalibrationData &data, |
| const CalibrationParameters &calibration_parameters) { |
| PoseFilter filter(calibration_parameters.initial_orientation, |
| calibration_parameters.imu_to_camera, |
| calibration_parameters.gyro_bias, |
| calibration_parameters.initial_state, |
| calibration_parameters.board_to_world, |
| calibration_parameters.imu_to_camera_translation, |
| calibration_parameters.gravity_scalar, |
| calibration_parameters.accelerometer_bias); |
| data.ReviewData(&filter); |
| filter.Plot(); |
| } |
| |
| } // namespace vision |
| } // namespace frc971 |