diff --git a/.gitignore b/.gitignore index 0fcd422..8e8e975 100644 --- a/.gitignore +++ b/.gitignore @@ -5,5 +5,6 @@ /docs /build /test/build/ +/test/results/ /ros/devel/ /ros/build/ diff --git a/config/go1.json b/config/go1.json index ade0fb6..2b33f33 100644 --- a/config/go1.json +++ b/config/go1.json @@ -45,6 +45,9 @@ "imu_angular_velocity_bias_covariance": [1e-5, 1e-5, 1e-5], "imu_linear_acceleration_covariance": [1e-2, 1e-2, 1e-2], "imu_linear_acceleration_bias_covariance": [1e-4, 1e-4, 1e-4], + + "use_contacts_in_base_estimation": false, + "base_linear_velocity_covariance": [1e-1, 1e-1, 1e-1], "contact_position_covariance": [1e-4, 1e-4, 1e-4], "contact_orientation_covariance": null, "contact_position_slip_covariance": [1e-6, 1e-6, 1e-6], diff --git a/config/go2.json b/config/go2.json index 42aba3e..c9ed8ad 100644 --- a/config/go2.json +++ b/config/go2.json @@ -45,6 +45,9 @@ "imu_angular_velocity_bias_covariance": [1e-5, 1e-5, 1e-5], "imu_linear_acceleration_covariance": [1e-2, 1e-2, 1e-2], "imu_linear_acceleration_bias_covariance": [1e-4, 1e-4, 1e-4], + + "use_contacts_in_base_estimation": false, + "base_linear_velocity_covariance": [1e-1, 1e-1, 1e-1], "contact_position_covariance": [1e-4, 1e-4, 1e-4], "contact_orientation_covariance": null, "contact_position_slip_covariance": [1e-6, 1e-6, 1e-6], diff --git a/config/nao.json b/config/nao.json index ebbedf7..90d3d0d 100644 --- a/config/nao.json +++ b/config/nao.json @@ -44,6 +44,9 @@ "imu_angular_velocity_bias_covariance": [1e-6, 1e-6, 1e-6], "imu_linear_acceleration_covariance": [1e-3, 1e-3, 1e-3], "imu_linear_acceleration_bias_covariance": [1e-5, 1e-5, 1e-5], + + "use_contacts_in_base_estimation": false, + "base_linear_velocity_covariance": [1e-1, 1e-1, 1e-1], "contact_position_covariance": [1e-6, 1e-6, 1e-6], "contact_orientation_covariance": [1e-3, 1e-3, 1e-3], "contact_position_slip_covariance": [1e-4, 1e-4, 1e-4], diff --git a/config/valkyrie.json b/config/valkyrie.json index a06908f..f91330a 100644 --- a/config/valkyrie.json +++ b/config/valkyrie.json @@ -40,10 +40,14 @@ "median_window": 13, "outlier_detection": false, "convergence_cycles": 0, + "imu_angular_velocity_covariance": [1e-4, 1e-4, 1e-4], "imu_angular_velocity_bias_covariance": [1e-6, 1e-6, 1e-6], "imu_linear_acceleration_covariance": [1e-3, 1e-3, 1e-3], "imu_linear_acceleration_bias_covariance": [1e-5, 1e-5, 1e-5], + + "use_contacts_in_base_estimation": false, + "base_linear_velocity_covariance": [1e-1, 1e-1, 1e-1], "contact_position_covariance": [1e-6, 1e-6, 1e-6], "contact_orientation_covariance": [1e-3, 1e-3, 1e-3], "contact_position_slip_covariance": [1e-4, 1e-4, 1e-4], @@ -56,6 +60,7 @@ "initial_base_position_covariance": [1.0, 1.0, 1.0], "initial_base_orientation_covariance": [1.0, 1.0, 1.0], "initial_base_linear_velocity_covariance": [1.0, 1.0, 1.0], + "initial_contact_position_covariance": [1.0, 1.0, 1.0], "initial_contact_orientation_covariance": [1.0, 1.0, 1.0], "initial_imu_linear_acceleration_bias_covariance": [1.0, 1.0, 1.0], diff --git a/core/src/BaseEKF.cpp b/core/src/BaseEKF.cpp new file mode 100644 index 0000000..88e7ee2 --- /dev/null +++ b/core/src/BaseEKF.cpp @@ -0,0 +1,262 @@ +/** + * Copyright (C) 2024 Stylianos Piperakis, Ownage Dynamics L.P. + * Serow is free software: you can redistribute it and/or modify it under the terms of the GNU + * General Public License as published by the Free Software Foundation, version 3. + * + * Serow is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without + * even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU + * General Public License for more details. + * + * You should have received a copy of the GNU General Public License along with Serow. If not, + * see . + **/ +#include "BaseEKF.hpp" + +#include "lie.hpp" + +namespace serow { + +void BaseEKF::init(const BaseState& state, + double g, double imu_rate, bool outlier_detection) { + g_ = Eigen::Vector3d(0.0, 0.0, -g); + outlier_detection_ = outlier_detection; + num_states_ = 15; + num_inputs_ = 12; + nominal_dt_ = 1.0 / imu_rate; + I_.setIdentity(num_states_, num_states_); + + // Initialize state indices + v_idx_ = Eigen::Array3i::LinSpaced(0, 3); + r_idx_ = v_idx_ + 3; + p_idx_ = r_idx_ + 3; + bg_idx_ = p_idx_ + 3; + ba_idx_ = bg_idx_ + 3; + + ng_idx_ = Eigen::Array3i::LinSpaced(0, 3); + na_idx_ = ng_idx_ + 3; + nbg_idx_ = na_idx_ + 3; + nba_idx_ = nbg_idx_ + 3; + + // Initialize the error state covariance + P_ = I_; + P_(v_idx_, v_idx_) = state.base_linear_velocity_cov; + P_(r_idx_, r_idx_) = state.base_orientation_cov; + P_(p_idx_, p_idx_) = state.base_position_cov; + P_(bg_idx_, bg_idx_) = state.imu_angular_velocity_bias_cov; + P_(ba_idx_, ba_idx_) = state.imu_linear_acceleration_bias_cov; + + // Compute some parts of the Input-Noise Jacobian once since they are constants + // gyro (0), acc (3), gyro_bias (6), acc_bias (9) + Lc_.setZero(num_states_, num_inputs_); + Lc_(v_idx_, na_idx_) = -Eigen::Matrix3d::Identity(); + Lc_(r_idx_, ng_idx_) = -Eigen::Matrix3d::Identity(); + Lc_(bg_idx_, nbg_idx_) = Eigen::Matrix3d::Identity(); + Lc_(ba_idx_, nba_idx_) = Eigen::Matrix3d::Identity(); + + std::cout << "Base EKF Initialized Successfully" << std::endl; +} + +std::tuple BaseEKF::computePredictionJacobians( + const BaseState& state, Eigen::Vector3d angular_velocity) { + angular_velocity -= state.imu_angular_velocity_bias; + const Eigen::Vector3d& v = state.base_linear_velocity; + const Eigen::Matrix3d& R = state.base_orientation.toRotationMatrix(); + + Eigen::MatrixXd Ac, Lc; + Lc = Lc_; + Lc(v_idx_, ng_idx_).noalias() = -lie::so3::wedge(v); + + Ac.setZero(num_states_, num_states_); + Ac(v_idx_, v_idx_).noalias() = -lie::so3::wedge(angular_velocity); + Ac(v_idx_, r_idx_).noalias() = lie::so3::wedge(R.transpose() * g_); + Ac(v_idx_, bg_idx_).noalias() = -lie::so3::wedge(v); + Ac(v_idx_, ba_idx_).noalias() = -Eigen::Matrix3d::Identity(); + Ac(r_idx_, r_idx_).noalias() = -lie::so3::wedge(angular_velocity); + Ac(r_idx_, bg_idx_).noalias() = -Eigen::Matrix3d::Identity(); + Ac(p_idx_, v_idx_).noalias() = R; + Ac(p_idx_, r_idx_).noalias() = -R * lie::so3::wedge(v); + + return std::make_tuple(Ac, Lc); +} + +BaseState BaseEKF::predict(const BaseState& state, const ImuMeasurement& imu) { + double dt = nominal_dt_; + if (last_imu_timestamp_.has_value()) { + dt = imu.timestamp - last_imu_timestamp_.value(); + } + if (dt < nominal_dt_ / 2) { + dt = nominal_dt_; + } + // Compute the state and input-state Jacobians + const auto& [Ac, Lc] = computePredictionJacobians(state, imu.angular_velocity); + // Euler Discretization - First order Truncation + Eigen::MatrixXd Ad = I_; + Ad += Ac * dt; + + Eigen::MatrixXd Qc = Eigen::MatrixXd::Zero(num_inputs_, num_inputs_); + // Covariance Q with full state + biases + Qc(ng_idx_, ng_idx_) = imu.angular_velocity_cov; + Qc(na_idx_, na_idx_) = imu.linear_acceleration_cov; + Qc(nbg_idx_, nbg_idx_) = imu.angular_velocity_bias_cov; + Qc(nba_idx_, nba_idx_) = imu.linear_acceleration_bias_cov; + + // Predict the state error covariance + Eigen::MatrixXd Qd = Ad * Lc * Qc * Lc.transpose() * Ad.transpose() * dt; + P_ = Ad * P_ * Ad.transpose() + Qd; + + // Predict the state + const BaseState& predicted_state = computeDiscreteDynamics( + state, dt, imu.angular_velocity, imu.linear_acceleration); + last_imu_timestamp_ = imu.timestamp; + return predicted_state; +} + +BaseState BaseEKF::computeDiscreteDynamics( + const BaseState& state, double dt, Eigen::Vector3d angular_velocity, + Eigen::Vector3d linear_acceleration) { + BaseState predicted_state = state; + angular_velocity -= state.imu_angular_velocity_bias; + linear_acceleration -= state.imu_linear_acceleration_bias; + + // Nonlinear Process Model + // Linear velocity + const Eigen::Vector3d& v = state.base_linear_velocity; + const Eigen::Matrix3d& R = state.base_orientation.toRotationMatrix(); + predicted_state.base_linear_velocity = v.cross(angular_velocity); + predicted_state.base_linear_velocity += R.transpose() * g_; + predicted_state.base_linear_velocity += linear_acceleration; + predicted_state.base_linear_velocity *= dt; + predicted_state.base_linear_velocity += v; + // Position + const Eigen::Vector3d& r = state.base_position; + predicted_state.base_position = R * v; + predicted_state.base_position *= dt; + predicted_state.base_position += r; + + // Biases + predicted_state.imu_angular_velocity_bias = state.imu_angular_velocity_bias; + predicted_state.imu_linear_acceleration_bias = state.imu_linear_acceleration_bias; + + // Orientation + predicted_state.base_orientation = + Eigen::Quaterniond(lie::so3::plus(R, angular_velocity * dt)).normalized(); + + return predicted_state; +} + +BaseState BaseEKF::updateWithOdometry(const BaseState& state, + const Eigen::Vector3d& base_position, + const Eigen::Quaterniond& base_orientation, + const Eigen::Matrix3d& base_position_cov, + const Eigen::Matrix3d& base_orientation_cov) { + BaseState updated_state = state; + + Eigen::MatrixXd H; + H.setZero(6, num_states_); + Eigen::MatrixXd R = Eigen::Matrix::Zero(); + + // Construct the innovation vector z + const Eigen::Vector3d& zp = base_position - state.base_position; + const Eigen::Vector3d& zq = lie::so3::minus(base_orientation, state.base_orientation); + Eigen::VectorXd z; + z.setZero(6); + z.head(3) = zp; + z.tail(3) = zq; + + // Construct the linearized measurement matrix H + H.block(0, p_idx_[0], 3, 3) = Eigen::Matrix3d::Identity(); + H.block(3, r_idx_[0], 3, 3) = Eigen::Matrix3d::Identity(); + + // Construct the measurement noise matrix R + R.topLeftCorner<3, 3>() = base_position_cov; + R.bottomRightCorner<3, 3>() = base_orientation_cov; + + const Eigen::Matrix s = R + H * P_ * H.transpose(); + const Eigen::MatrixXd& K = P_ * H.transpose() * s.inverse(); + const Eigen::VectorXd& dx = K * z; + + P_ = (I_ - K * H) * P_; + updateState(updated_state, dx, P_); + + return updated_state; +} + + +BaseState BaseEKF::updateWithTwist(const BaseState& state, + const Eigen::Vector3d& base_linear_velocity, + const Eigen::Matrix3d& base_linear_velocity_cov) { + BaseState updated_state = state; + + const Eigen::Matrix3d R_world_to_base = state.base_orientation.toRotationMatrix(); + // Construct the linearized measurement matrix H + Eigen::MatrixXd H; + H.setZero(3, num_states_); + H.block(0, v_idx_[0], 3, 3) = R_world_to_base; + H.block(0, r_idx_[0], 3, 3) = -R_world_to_base * lie::so3::wedge(state.base_linear_velocity); + + // Construct the innovation vector z + const Eigen::Vector3d z = base_linear_velocity - R_world_to_base * state.base_linear_velocity; + + // Construct the measurement noise matrix R + const Eigen::Matrix3d R = base_linear_velocity_cov; + + const Eigen::Matrix3d s = R + H * P_ * H.transpose(); + const Eigen::MatrixXd K = P_ * H.transpose() * s.inverse(); + const Eigen::VectorXd dx = K * z; + + P_ = (I_ - K * H) * P_; + updateState(updated_state, dx, P_); + + return updated_state; +} + + +BaseState BaseEKF::updateStateCopy(const BaseState& state, const Eigen::VectorXd& dx, + const Eigen::MatrixXd& P) const { + BaseState updated_state = state; + updated_state.base_position += dx(p_idx_); + updated_state.base_position_cov = P(p_idx_, p_idx_); + updated_state.base_linear_velocity += dx(v_idx_); + updated_state.base_linear_velocity_cov = P(v_idx_, v_idx_); + updated_state.base_orientation = + Eigen::Quaterniond(lie::so3::plus(state.base_orientation.toRotationMatrix(), dx(r_idx_))) + .normalized(); + updated_state.base_orientation_cov = P(r_idx_, r_idx_); + updated_state.imu_angular_velocity_bias += dx(bg_idx_); + updated_state.imu_angular_velocity_bias_cov = P(bg_idx_, bg_idx_); + updated_state.imu_linear_acceleration_bias += dx(ba_idx_); + updated_state.imu_linear_acceleration_bias_cov = P(ba_idx_, ba_idx_); + return updated_state; +} + +void BaseEKF::updateState(BaseState& state, const Eigen::VectorXd& dx, + const Eigen::MatrixXd& P) const { + state.base_position += dx(p_idx_); + state.base_position_cov = P(p_idx_, p_idx_); + state.base_linear_velocity += dx(v_idx_); + state.base_linear_velocity_cov = P(v_idx_, v_idx_); + state.base_orientation = + Eigen::Quaterniond(lie::so3::plus(state.base_orientation.toRotationMatrix(), dx(r_idx_))) + .normalized(); + state.base_orientation_cov = P(r_idx_, r_idx_); + state.imu_angular_velocity_bias += dx(bg_idx_); + state.imu_angular_velocity_bias_cov = P(bg_idx_, bg_idx_); + state.imu_linear_acceleration_bias += dx(ba_idx_); + state.imu_linear_acceleration_bias_cov = P(ba_idx_, ba_idx_); +} + +BaseState BaseEKF::update(const BaseState& state, const KinematicMeasurement& kin, + std::optional odom) { + BaseState updated_state = + updateWithTwist(state, kin.base_linear_velocity, kin.base_linear_velocity_cov); + + if (odom.has_value()) { + updated_state = + updateWithOdometry(updated_state, odom->base_position, odom->base_orientation, + odom->base_position_cov, odom->base_orientation_cov); + } + + return updated_state; +} + +} // namespace serow diff --git a/core/src/BaseEKF.hpp b/core/src/BaseEKF.hpp new file mode 100644 index 0000000..b8bb3b2 --- /dev/null +++ b/core/src/BaseEKF.hpp @@ -0,0 +1,162 @@ +/** + * Copyright (C) 2024 Stylianos Piperakis, Ownage Dynamics L.P. + * Serow is free software: you can redistribute it and/or modify it under the terms of the GNU + * General Public License as published by the Free Software Foundation, version 3. + * + * Serow is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without + * even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU + * General Public License for more details. + * + * You should have received a copy of the GNU General Public License along with Serow. If not, + * see . + **/ + +/** + * @file BaseEKF.hpp + * @brief Implementation of Extended Kalman Filter (EKF) for state estimation in humanoid robots. + * This EKF fuses data from an Inertial Measurement Unit (IMU), base linear velocity + * measurement, and optionally external odometry (e.g., Visual Odometry or Lidar + * Odometry). It models the state of the robot including base position, velocity, + * orientation in world frame coordinates as well as the gyro and accelerometer biases. + * @author Stylianos Piperakis + * @details The state estimation involves predicting and updating the robot's state based on sensor + * measurements and the robot's dynamics. The EKF integrates outlier detection mechanisms + * to improve state estimation robustness, incorporating beta distribution parameters and + * digamma function approximations. More information on the nonlinear state estimation for + * humanoid robot walking can be found in: + * https://www.researchgate.net/publication/326194869_Nonlinear_State_Estimation_for_Humanoid_Robot_Walking + */ + +#pragma once + +#include + +#include "OutlierDetector.hpp" +#include "Measurement.hpp" // Includes various sensor measurements +#include "State.hpp" // Includes definitions of robot state variables + +namespace serow { + +/** + * @class BaseEKF + * @brief Implements an Extended Kalman Filter (EKF) for state estimation in humanoid robots, + * specifically for fusing IMU data, base linear velocity, and external odometry. + */ +class BaseEKF { + public: + /** + * @brief Initializes the EKF with the initial robot state and other parameters. + * @param state Initial state of the robot. + * @param g Acceleration due to gravity. + * @param imu_rate IMU update rate. + * @param outlier_detection Flag indicating if outlier detection mechanisms should be enabled. + */ + void init(const BaseState& state, double g, double imu_rate, bool outlier_detection = false); + + /** + * @brief Predicts the robot's state forward based on IMU. + * @param state Current state of the robot. + * @param imu IMU measurements. + * @return Predicted state after applying prediction step of the EKF. + */ + BaseState predict(const BaseState& state, const ImuMeasurement& imu); + + /** + * @brief Updates the robot's state based on kinematic measurements and optionally odometry + * measurements. + * @param state Current state of the robot. + * @param kin Kinematic measurements. + * @param odom Optional odometry measurements. + * @param terrain Optional terrain measurements. + * @return Updated state after applying update step of the EKF. + */ + BaseState update(const BaseState& state, const KinematicMeasurement& kin, + std::optional odom = std::nullopt); + + private: + int num_states_{}; ///< Number of state variables. + int num_inputs_{}; ///< Number of input variables. + bool outlier_detection_{}; ///< Flag indicating if outlier detection is enabled. + double nominal_dt_{}; ///< Nominal sampling time for prediction step. + Eigen::Vector3d g_; ///< Gravity vector. + // State indices + Eigen::Array3i v_idx_; ///< Indices for velocity state variables. + Eigen::Array3i r_idx_; ///< Indices for orientation state variables. + Eigen::Array3i p_idx_; ///< Indices for position state variables. + Eigen::Array3i bg_idx_; ///< Indices for gyro bias state variables. + Eigen::Array3i ba_idx_; ///< Indices for accelerometer bias state variables. + // Input indices + Eigen::Array3i ng_idx_; ///< Indices for IMU input variables. + Eigen::Array3i na_idx_; ///< Indices for kinematic input variables. + Eigen::Array3i nbg_idx_; ///< Indices for gyro bias input variables. + Eigen::Array3i nba_idx_; ///< Indices for accelerometer bias input variables. + std::optional last_imu_timestamp_; ///< Timestamp of the last IMU measurement. + + /// Error Covariance, Linearized state transition model, Identity matrix, state uncertainty + /// matrix 15 x 15 + Eigen::Matrix I_, P_; + /// Linearized state-input model 15 x 12 + Eigen::Matrix Lc_; + + OutlierDetector contact_outlier_detector; ///< Outlier detector instance. + + /** + * @brief Computes discrete dynamics for the prediction step of the EKF. + * @param state Current state of the robot. + * @param dt Time step for prediction. + * @param angular_velocity Angular velocity measurements. + * @param linear_acceleration Linear acceleration measurements. + * @return Predicted state after applying discrete dynamics. + */ + BaseState computeDiscreteDynamics( + const BaseState& state, double dt, Eigen::Vector3d angular_velocity, + Eigen::Vector3d linear_acceleration); + + /** + * @brief Computes Jacobians for the prediction step of the EKF. + * @param state Current state of the robot. + * @param angular_velocity Angular velocity measurements. + * @return Tuple containing prediction Jacobians (state transition and input models). + */ + std::tuple computePredictionJacobians( + const BaseState& state, Eigen::Vector3d angular_velocity); + + BaseState updateWithTwist(const BaseState& state, + const Eigen::Vector3d& base_linear_velocity, + const Eigen::Matrix3d& base_linear_velocity_cov); + + /** + * @brief Updates the robot's state based on odometry measurements. + * @param state Current state of the robot. + * @param base_position Position of the robot's base. + * @param base_orientation Orientation of the robot's base. + * @param base_position_cov Covariance of base position measurements. + * @param base_orientation_cov Covariance of base orientation measurements. + * @return Updated state after applying odometry updates. + */ + BaseState updateWithOdometry(const BaseState& state, const Eigen::Vector3d& base_position, + const Eigen::Quaterniond& base_orientation, + const Eigen::Matrix3d& base_position_cov, + const Eigen::Matrix3d& base_orientation_cov); + + /** + * @brief Updates the state of the robot with the provided state change and covariance matrix. + * @param state Current state of the robot (will be updated in-place). + * @param dx State change vector. + * @param P Covariance matrix of the state change. + */ + void updateState(BaseState& state, const Eigen::VectorXd& dx, const Eigen::MatrixXd& P) const; + + /** + * @brief Updates a copy of the robot's state with the provided state change and covariance + * matrix. + * @param state Current state of the robot (will not be modified). + * @param dx State change vector. + * @param P Covariance matrix of the state change. + * @return Updated state after applying the state change. + */ + BaseState updateStateCopy(const BaseState& state, const Eigen::VectorXd& dx, + const Eigen::MatrixXd& P) const; +}; + +} // namespace serow diff --git a/core/src/CMakeLists.txt b/core/src/CMakeLists.txt index 8fe88f8..aecc5b9 100644 --- a/core/src/CMakeLists.txt +++ b/core/src/CMakeLists.txt @@ -5,16 +5,19 @@ find_package(nlohmann_json REQUIRED) set(serow_src ${serow_src} Serow.cpp State.cpp + BaseEKF.cpp ContactEKF.cpp CoMEKF.cpp LegOdometry.cpp ButterworthLPF.cpp Differentiator.cpp - DerivativeEstimator.cpp) + DerivativeEstimator.cpp + OutlierDetector.cpp) set(serow_headers ${serow_headers} Serow.hpp State.hpp + BaseEKF.hpp ContactEKF.hpp CoMEKF.hpp LegOdometry.hpp @@ -26,7 +29,8 @@ set(serow_headers ${serow_headers} Mahony.hpp Measurement.hpp MovingMedianFilter.hpp - RobotKinematics.hpp) + RobotKinematics.hpp + OutlierDetector.hpp) add_library(serow SHARED ${serow_headers} ${serow_src}) target_link_libraries(serow PRIVATE Eigen3::Eigen pinocchio::pinocchio nlohmann_json::nlohmann_json) diff --git a/core/src/ContactEKF.cpp b/core/src/ContactEKF.cpp index 26b1951..ec9fa8a 100644 --- a/core/src/ContactEKF.cpp +++ b/core/src/ContactEKF.cpp @@ -16,51 +16,6 @@ namespace serow { -double OutlierDetector::computePsi(double xxx) { - double result = 0; - double xx, xx2, xx4; - for (; xxx < 7; ++xxx) { - result -= 1 / xxx; - } - - xxx -= 1.0 / 2.0; - xx = 1.0 / xxx; - xx2 = xx * xx; - xx4 = xx2 * xx2; - result += std::log(xxx) + (1.0 / 24.0) * xx2 - (7.0 / 960.0) * xx4 + - (31.0 / 8064.0) * xx4 * xx2 - (127.0 / 30720.0) * xx4 * xx4; - return result; -} - -void OutlierDetector::init() { - zeta = 1.0; - e_t = e_0; - f_t = f_0; -} - -void OutlierDetector::estimate(const Eigen::Matrix3d& BetaT, const Eigen::Matrix3d& R) { - double efpsi = computePsi(e_t + f_t); - double lnp = computePsi(e_t) - efpsi; - double ln1_p = computePsi(f_t) - efpsi; - - double pzeta_1 = std::exp(lnp - 0.5 * (BetaT * R.inverse()).trace()); - double pzeta_0 = std::exp(ln1_p); - - // Normalization factor - double norm_factor = 1.0 / (pzeta_1 + pzeta_0); - - // p(zeta) are now proper probabilities - pzeta_1 = norm_factor * pzeta_1; - pzeta_0 = norm_factor * pzeta_0; - - // Mean of Bernulli - zeta = pzeta_1 / (pzeta_1 + pzeta_0); - - // Update epsilon and f - e_t = e_0 + zeta; - f_t = f_0 + 1.0 - zeta; -} - void ContactEKF::init(const BaseState& state, std::set contacts_frame, bool point_feet, double g, double imu_rate, bool outlier_detection) { num_leg_end_effectors_ = contacts_frame.size(); @@ -414,9 +369,9 @@ BaseState ContactEKF::updateWithOdometry(const BaseState& state, R.topLeftCorner<3, 3>() = base_position_cov; R.bottomRightCorner<3, 3>() = base_orientation_cov; - const Eigen::Matrix3d& s = R + H * P_ * H.transpose(); - const Eigen::MatrixXd& K = P_ * H.transpose() * s.inverse(); - const Eigen::VectorXd& dx = K * z; + const Eigen::Matrix s = R + H * P_ * H.transpose(); + const Eigen::MatrixXd K = P_ * H.transpose() * s.inverse(); + const Eigen::VectorXd dx = K * z; P_ = (I_ - K * H) * P_; updateState(updated_state, dx, P_); diff --git a/core/src/ContactEKF.hpp b/core/src/ContactEKF.hpp index 3a9565a..e3c92b6 100644 --- a/core/src/ContactEKF.hpp +++ b/core/src/ContactEKF.hpp @@ -32,45 +32,12 @@ #include +#include "OutlierDetector.hpp" #include "Measurement.hpp" // Includes various sensor measurements #include "State.hpp" // Includes definitions of robot state variables namespace serow { -/** - * @struct OutlierDetector - * @brief Implements outlier detection mechanisms using beta distribution parameters for state - * estimation in humanoid robots. - */ -struct OutlierDetector { - double zeta = 1.0; ///< Parameter for outlier detection: https://www.researchgate.net/publication/334745931_Outlier-Robust_State_Estimation_for_Humanoid_Robots - double f_0 = 0.1; ///< Parameter for outlier detection: https://www.researchgate.net/publication/334745931_Outlier-Robust_State_Estimation_for_Humanoid_Robots - double e_0 = 0.9; ///< Parameter for outlier detection: https://www.researchgate.net/publication/334745931_Outlier-Robust_State_Estimation_for_Humanoid_Robots - double f_t = 0.1; ///< Parameter for outlier detection: https://www.researchgate.net/publication/334745931_Outlier-Robust_State_Estimation_for_Humanoid_Robots - double e_t = 0.9; ///< Parameter for outlier detection: https://www.researchgate.net/publication/334745931_Outlier-Robust_State_Estimation_for_Humanoid_Robots - double threshold = 1e-5; - size_t iters = 4; ///< Number of iterations for outlier detection - - /** - * @brief Computes the digamma function approximation. - * @param x Argument for digamma function. - * @return Computed value of digamma function. - */ - double computePsi(double x); - - /** - * @brief Initializes the outlier detection process. - */ - void init(); - - /** - * @brief Estimates the outlier indicator zeta based on provided matrices. - * @param BetaT Matrix used in outlier estimation. - * @param R Matrix used in outlier estimation. - */ - void estimate(const Eigen::Matrix3d& BetaT, const Eigen::Matrix3d& R); -}; - /** * @class ContactEKF * @brief Implements an Extended Kalman Filter (EKF) for state estimation in humanoid robots, diff --git a/core/src/Measurement.hpp b/core/src/Measurement.hpp index 29a68e4..5346244 100644 --- a/core/src/Measurement.hpp +++ b/core/src/Measurement.hpp @@ -73,6 +73,7 @@ struct GroundReactionForceMeasurement { */ struct KinematicMeasurement { double timestamp{}; ///< Timestamp of the measurement (s) + Eigen::Vector3d base_linear_velocity{Eigen::Vector3d::Zero()}; ///< Base linear velocity (m/s) std::map contacts_status; ///< Map of contact status for different parts (0 or 1) std::map contacts_probability; ///< Map of contact probabilities ([0, 1]) std::map contacts_position; ///< Map of contact positions relative to base frame (m) @@ -82,6 +83,7 @@ struct KinematicMeasurement { Eigen::Vector3d com_angular_momentum_derivative{Eigen::Vector3d::Zero()}; ///< Derivative of center of mass (COM) angular momentum (Nm) Eigen::Vector3d com_position{Eigen::Vector3d::Zero()}; ///< Center of mass (COM) position (m) Eigen::Vector3d com_linear_acceleration{Eigen::Vector3d::Zero()}; ///< Center of mass (COM) linear acceleration (m/s^2) + Eigen::Matrix3d base_linear_velocity_cov{Eigen::Matrix3d::Identity()}; ///< Covariance of base linear velocity (m^2/s^2) Eigen::Matrix3d position_slip_cov{Eigen::Matrix3d::Identity()}; ///< Covariance of position slip (m^2) Eigen::Matrix3d orientation_slip_cov{Eigen::Matrix3d::Identity()}; ///< Covariance of orientation slip (rad^2) Eigen::Matrix3d position_cov{Eigen::Matrix3d::Identity()}; ///< Covariance of position (m^2) diff --git a/core/src/OutlierDetector.cpp b/core/src/OutlierDetector.cpp new file mode 100644 index 0000000..c6fd015 --- /dev/null +++ b/core/src/OutlierDetector.cpp @@ -0,0 +1,64 @@ +/** + * Copyright (C) 2024 Stylianos Piperakis, Ownage Dynamics L.P. + * Serow is free software: you can redistribute it and/or modify it under the terms of the GNU + * General Public License as published by the Free Software Foundation, version 3. + * + * Serow is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without + * even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU + * General Public License for more details. + * + * You should have received a copy of the GNU General Public License along with Serow. If not, + * see . + **/ +#include "OutlierDetector.hpp" + + +namespace serow { + +double OutlierDetector::computePsi(double xxx) { + double result = 0; + double xx, xx2, xx4; + for (; xxx < 7; ++xxx) { + result -= 1 / xxx; + } + + xxx -= 1.0 / 2.0; + xx = 1.0 / xxx; + xx2 = xx * xx; + xx4 = xx2 * xx2; + result += std::log(xxx) + (1.0 / 24.0) * xx2 - (7.0 / 960.0) * xx4 + + (31.0 / 8064.0) * xx4 * xx2 - (127.0 / 30720.0) * xx4 * xx4; + return result; +} + +void OutlierDetector::init() { + zeta = 1.0; + e_t = e_0; + f_t = f_0; +} + +void OutlierDetector::estimate(const Eigen::Matrix3d& BetaT, const Eigen::Matrix3d& R) { + double efpsi = computePsi(e_t + f_t); + double lnp = computePsi(e_t) - efpsi; + double ln1_p = computePsi(f_t) - efpsi; + + double pzeta_1 = std::exp(lnp - 0.5 * (BetaT * R.inverse()).trace()); + double pzeta_0 = std::exp(ln1_p); + + // Normalization factor + double norm_factor = 1.0 / (pzeta_1 + pzeta_0); + + // p(zeta) are now proper probabilities + pzeta_1 = norm_factor * pzeta_1; + pzeta_0 = norm_factor * pzeta_0; + + // Mean of Bernulli + zeta = pzeta_1 / (pzeta_1 + pzeta_0); + + // Update epsilon and f + e_t = e_0 + zeta; + f_t = f_0 + 1.0 - zeta; +} + + +} // namespace serow diff --git a/core/src/OutlierDetector.hpp b/core/src/OutlierDetector.hpp new file mode 100644 index 0000000..2a5292e --- /dev/null +++ b/core/src/OutlierDetector.hpp @@ -0,0 +1,76 @@ +/** + * Copyright (C) 2024 Stylianos Piperakis, Ownage Dynamics L.P. + * Serow is free software: you can redistribute it and/or modify it under the terms of the GNU + * General Public License as published by the Free Software Foundation, version 3. + * + * Serow is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without + * even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU + * General Public License for more details. + * + * You should have received a copy of the GNU General Public License along with Serow. If not, + * see . + **/ + +/** + * @file ContactEKF.hpp + * @brief Implementation of Extended Kalman Filter (EKF) for state estimation in humanoid robots. + * This EKF fuses data from an Inertial Measurement Unit (IMU), relative to the base leg + * contact measurements, and optionally external odometry (e.g., Visual Odometry or Lidar + * Odometry). It models the state of the robot including base position, velocity, + * orientation, gyro and accelerometer biases, leg contact positions, and contact orientations in + * world frame coordinates. + * @author Stylianos Piperakis + * @details The state estimation involves predicting and updating the robot's state based on sensor + * measurements and the robot's dynamics. The EKF integrates outlier detection mechanisms + * to improve state estimation robustness, incorporating beta distribution parameters and + * digamma function approximations. More information on the nonlinear state estimation for + * humanoid robot walking can be found in: + * https://www.researchgate.net/publication/326194869_Nonlinear_State_Estimation_for_Humanoid_Robot_Walking + */ + +#pragma once + +#ifdef __linux__ +#include +#else +#include +#endif +#include + +namespace serow { + +/** + * @struct OutlierDetector + * @brief Implements outlier detection mechanisms using beta distribution parameters for state + * estimation in humanoid robots. + */ +struct OutlierDetector { + double zeta = 1.0; ///< Parameter for outlier detection: https://www.researchgate.net/publication/334745931_Outlier-Robust_State_Estimation_for_Humanoid_Robots + double f_0 = 0.1; ///< Parameter for outlier detection: https://www.researchgate.net/publication/334745931_Outlier-Robust_State_Estimation_for_Humanoid_Robots + double e_0 = 0.9; ///< Parameter for outlier detection: https://www.researchgate.net/publication/334745931_Outlier-Robust_State_Estimation_for_Humanoid_Robots + double f_t = 0.1; ///< Parameter for outlier detection: https://www.researchgate.net/publication/334745931_Outlier-Robust_State_Estimation_for_Humanoid_Robots + double e_t = 0.9; ///< Parameter for outlier detection: https://www.researchgate.net/publication/334745931_Outlier-Robust_State_Estimation_for_Humanoid_Robots + double threshold = 1e-5; + size_t iters = 4; ///< Number of iterations for outlier detection + + /** + * @brief Computes the digamma function approximation. + * @param x Argument for digamma function. + * @return Computed value of digamma function. + */ + double computePsi(double x); + + /** + * @brief Initializes the outlier detection process. + */ + void init(); + + /** + * @brief Estimates the outlier indicator zeta based on provided matrices. + * @param BetaT Matrix used in outlier estimation. + * @param R Matrix used in outlier estimation. + */ + void estimate(const Eigen::Matrix3d& BetaT, const Eigen::Matrix3d& R); +}; + +} // namespace serow diff --git a/core/src/Serow.cpp b/core/src/Serow.cpp index ccdfc14..fbc1157 100644 --- a/core/src/Serow.cpp +++ b/core/src/Serow.cpp @@ -302,6 +302,18 @@ bool Serow::initialize(const std::string& config_file) { return false; } + if (!config["use_contacts_in_base_estimation"].is_boolean()) { + std::cerr << "Configuration: use_contacts_in_base_estimation must be boolean \n"; + return false; + } + params_.is_contact_ekf = config["use_contacts_in_base_estimation"]; + + if (!config["base_linear_velocity_covariance"].is_array() || + config["base_linear_velocity_covariance"].size() != 3) { + std::cerr << "Configuration: base_linear_velocity_covariance must be an array of 3 elements \n"; + return false; + } + if (!config["contact_position_covariance"].is_array() || config["contact_position_covariance"].size() != 3) { std::cerr << "Configuration: contact_position_covariance must be an array of 3 elements \n"; @@ -421,11 +433,16 @@ bool Serow::initialize(const std::string& config_file) { return false; } params_.linear_acceleration_bias_cov[i] = config["imu_linear_acceleration_bias_covariance"][i]; + if (!config["base_linear_velocity_covariance"][i].is_number_float()) { + std::cerr << "Configuration: base_linear_velocity_covariance[" << i << "] must be float \n"; + return false; + } + params_.base_linear_velocity_cov[i] = config["base_linear_velocity_covariance"][i]; if (!config["contact_position_covariance"][i].is_number_float()) { std::cerr << "Configuration: contact_position_covariance[" << i << "] must be float \n"; return false; } - params_.contact_position_cov[i] = config["contact_position_covariance"][i]; + params_.contact_position_cov[i] = config["contact_position_covariance"][i]; if (!config["contact_orientation_covariance"].is_null()) { if (!config["contact_orientation_covariance"][i].is_number_float()) { std::cerr << "Configuration: contact_orientation_covariance[" << i @@ -805,8 +822,13 @@ void Serow::filter(ImuMeasurement imu, std::map j leg_odometry_ = std::make_unique( base_to_foot_positions, base_to_foot_orientations, params_.mass, params_.tau_0, params_.tau_1, params_.joint_rate, params_.g, params_.eps); - base_estimator_.init(state.base_state_, state.getContactsFrame(), state.isPointFeet(), - params_.g, params_.imu_rate, params_.outlier_detection); + + if (params_.is_contact_ekf) { + base_estimator_con_.init(state.base_state_, state.getContactsFrame(), state.isPointFeet(), + params_.g, params_.imu_rate, params_.outlier_detection); + } else { + base_estimator_.init(state.base_state_, params_.g, params_.imu_rate, params_.outlier_detection); + } com_estimator_.init(state.centroidal_state_, params_.mass, params_.g, params_.force_torque_rate); } @@ -828,6 +850,8 @@ void Serow::filter(ImuMeasurement imu, std::map j KinematicMeasurement kin; kin.contacts_status = state.contact_state_.contacts_status; kin.contacts_probability = state.contact_state_.contacts_probability; + kin.base_linear_velocity = leg_odometry_->getBaseLinearVelocity(); + kin.base_linear_velocity_cov = params_.base_linear_velocity_cov.asDiagonal(); kin.contacts_position = leg_odometry_->getContactPositions(); kin.position_cov = params_.contact_position_cov.asDiagonal(); kin.position_slip_cov = params_.contact_position_slip_cov.asDiagonal(); @@ -855,8 +879,11 @@ void Serow::filter(ImuMeasurement imu, std::map j // Call the base estimator predict step utilizing imu and contact status measurements state.base_state_.timestamp = imu.timestamp; - state.base_state_ = base_estimator_.predict(state.base_state_, imu, kin); - + if (params_.is_contact_ekf) { + state.base_state_ = base_estimator_con_.predict(state.base_state_, imu, kin); + } else { + state.base_state_ = base_estimator_.predict(state.base_state_, imu); + } // Call the base estimator update step by employing relative contact pose, odometry and terrain // measurements if (odom.has_value()) { @@ -869,7 +896,25 @@ void Serow::filter(ImuMeasurement imu, std::map j params_.T_base_to_odom.linear().transpose(); } state.base_state_.timestamp = kin.timestamp; - state.base_state_ = base_estimator_.update(state.base_state_, kin, odom, terrain_); + + if (params_.is_contact_ekf) { + state.base_state_ = base_estimator_con_.update(state.base_state_, kin, odom, terrain_); + } else { + state.base_state_ = base_estimator_.update(state.base_state_, kin, odom); + std::map con_orient; + for (const auto& frame : state.getContactsFrame()) { + const Eigen::Isometry3d base_pose = state.getBasePose(); + state.base_state_.contacts_position[frame] = + base_pose * kin.contacts_position.at(frame); + if (!state.isPointFeet()) { + con_orient[frame] = Eigen::Quaterniond( + base_pose.linear() * kin.contacts_orientation->at(frame).toRotationMatrix()); + } + } + if (!state.isPointFeet()) { + state.base_state_.contacts_orientation = std::move(con_orient); + } + } // Estimate the angular acceleration using the gyro velocity if (!gyro_derivative_estimator) { @@ -935,10 +980,11 @@ void Serow::filter(ImuMeasurement imu, std::map j double den = 0.0; for (const auto& frame : state.getContactsFrame()) { grf.timestamp = ft->at(frame).timestamp; + const Eigen::Isometry3d foot_pose = state.getFootPose(frame); if (state.contact_state_.contacts_probability.at(frame) > 0.0) { - grf.force += state.getContactPose(frame)->linear() * ft->at(frame).force; + grf.force += foot_pose.linear() * ft->at(frame).force; grf.cop += state.contact_state_.contacts_probability.at(frame) * - (*state.getContactPose(frame) * ft->at(frame).cop); + (foot_pose * ft->at(frame).cop); den += state.contact_state_.contacts_probability.at(frame); } } diff --git a/core/src/Serow.hpp b/core/src/Serow.hpp index 1fbc0af..7c77312 100644 --- a/core/src/Serow.hpp +++ b/core/src/Serow.hpp @@ -16,6 +16,7 @@ #include #include +#include "BaseEKF.hpp" #include "CoMEKF.hpp" #include "ContactDetector.hpp" #include "ContactEKF.hpp" @@ -195,6 +196,8 @@ class Serow { /// @brief virtual flat terrain measurement uncertainty (m^2), only applies if /// is_flat_terrain = true double terrain_height_covariance{}; + bool is_contact_ekf{}; + Eigen::Vector3d base_linear_velocity_cov{Eigen::Vector3d::Zero()}; }; /// @brief SEROW's configuration @@ -211,7 +214,8 @@ class Serow { State state_; /// @brief base estimator that fuses base IMU, leg end-effector contact and relative to the base /// leg kinematic measurements - ContactEKF base_estimator_; + ContactEKF base_estimator_con_; + BaseEKF base_estimator_; /// @brief coM estimator that fuses ground reaction force, base IMU, and CoM kinematic /// measurements CoMEKF com_estimator_; diff --git a/core/src/State.cpp b/core/src/State.cpp index 6c83924..5df6d0c 100644 --- a/core/src/State.cpp +++ b/core/src/State.cpp @@ -458,6 +458,13 @@ const Eigen::Quaterniond& State::getFootOrientation(const std::string& frame_nam return base_state_.feet_orientation.at(frame_name); } +Eigen::Isometry3d State::getFootPose(const std::string& frame_name) const { + Eigen::Isometry3d foot_pose = Eigen::Isometry3d::Identity(); + foot_pose.linear() = base_state_.feet_orientation.at(frame_name).toRotationMatrix(); + foot_pose.translation() = base_state_.feet_position.at(frame_name); + return foot_pose; +} + const Eigen::Vector3d& State::getFootLinearVelocity(const std::string& frame_name) const { return base_state_.feet_linear_velocity.at(frame_name); } diff --git a/core/src/State.hpp b/core/src/State.hpp index 053823b..760bf39 100644 --- a/core/src/State.hpp +++ b/core/src/State.hpp @@ -189,6 +189,8 @@ class State { const Eigen::Vector3d& getFootPosition(const std::string& frame_name) const; /// Returns the foot frame 3D orientation in world frame coordinates const Eigen::Quaterniond& getFootOrientation(const std::string& frame_name) const; + /// Returns the foot frame 3D pose in world frame coordinates as a rigid transformation + Eigen::Isometry3d getFootPose(const std::string& frame_name) const; /// Returns the foot frame 3D linear velocity in world frame coordinates const Eigen::Vector3d& getFootLinearVelocity(const std::string& frame_name) const; /// Returns the foot frame 3D angular velocity in world frame coordinates diff --git a/test/nao_test.cpp b/test/nao_test.cpp index 9af9fef..6d64dd2 100644 --- a/test/nao_test.cpp +++ b/test/nao_test.cpp @@ -61,7 +61,6 @@ TEST(SerowTests, NaoTest) { std::map force_torque; force_torque.insert({"l_ankle", ft}); force_torque.insert({"r_ankle", ft}); - auto t0 = std::chrono::high_resolution_clock::now(); SEROW.filter(imu, joints, force_torque); auto t1 = std::chrono::high_resolution_clock::now(); diff --git a/test/results/go1_serow_estimates.txt b/test/results/go1_serow_estimates.txt deleted file mode 100644 index 7ca5831..0000000 --- a/test/results/go1_serow_estimates.txt +++ /dev/null @@ -1,2681 +0,0 @@ -4.2834e-08 0.00214501 0.000916886 -0.010986 -0.00159633 -0.00106215 -0.00310359 0.999993 -4.28366e-08 0.00218104 0.000928601 -0.0111725 -0.00160159 -0.00105812 -0.00313135 0.999993 -4.28391e-08 0.00221742 0.000940224 -0.0113598 -0.00159601 -0.0010503 -0.00314745 0.999993 -4.28416e-08 0.00225417 0.000951755 -0.0115479 -0.00159628 -0.00104811 -0.00315457 0.999993 -4.28441e-08 0.00229142 0.000963507 -0.0117376 -0.00161514 -0.00104239 -0.00315819 0.999993 -4.28466e-08 0.00232918 0.000975478 -0.0119288 -0.00162959 -0.0010327 -0.0031787 0.999993 -4.28491e-08 0.00236729 0.000987501 -0.0121212 -0.0016271 -0.00103091 -0.00317918 0.999993 -4.28516e-08 0.00240577 0.000999577 -0.0123148 -0.00162273 -0.00103008 -0.00318698 0.999993 -4.28541e-08 0.00244461 0.00101191 -0.0125098 -0.00163355 -0.00102824 -0.00318724 0.999993 -4.28566e-08 0.00248384 0.00102451 -0.0127063 -0.00164126 -0.00102233 -0.00320171 0.999993 -4.28591e-08 0.00252319 0.00103716 -0.0129038 -0.00164424 -0.00101954 -0.0032108 0.999993 -4.28616e-08 0.00256268 0.00104987 -0.0131025 -0.00164189 -0.00102076 -0.00321982 0.999993 -4.28641e-08 0.00260251 0.00106261 -0.0133027 -0.00164871 -0.00100635 -0.00322199 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