140 lines
4.9 KiB
C++
140 lines
4.9 KiB
C++
/*
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* Copyright (c) 2014, 2015, 2016, Charles River Analytics, Inc.
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*/
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#ifndef ROBOT_LOCALIZATION_UKF_H
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#define ROBOT_LOCALIZATION_UKF_H
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#include "robot_localization/filter_base.h"
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#include <fstream>
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#include <vector>
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#include <set>
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#include <queue>
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namespace RobotLocalization
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{
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//! @brief Unscented Kalman filter class
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//!
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//! Implementation of an unscenter Kalman filter (UKF). This
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//! class derives from FilterBase and overrides the predict()
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//! and correct() methods in keeping with the discrete time
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//! UKF algorithm. The algorithm was derived from the UKF
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//! Wikipedia article at
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//! (http://en.wikipedia.org/wiki/Kalman_filter#Unscented_Kalman_filter)
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//! ...and this paper:
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//! J. J. LaViola, Jr., “A comparison of unscented and extended Kalman
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//! filtering for estimating quaternion motion,” in Proc. American Control
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//! Conf., Denver, CO, June 4–6, 2003, pp. 2435–2440
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//! Obtained here: http://www.cs.ucf.edu/~jjl/pubs/laviola_acc2003.pdf
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//!
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class Ukf: public FilterBase
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{
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public:
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//! @brief Constructor for the Ukf class
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//!
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//! @param[in] args - Generic argument container. It is assumed
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//! that args[0] constains the alpha parameter, args[1] contains
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//! the kappa parameter, and args[2] contains the beta parameter.
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//!
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explicit Ukf(std::vector<double> args);
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//! @brief Destructor for the Ukf class
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//!
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~Ukf();
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//! @brief Carries out the correct step in the predict/update cycle.
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//!
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//! @param[in] measurement - The measurement to fuse with our estimate
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//!
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void correct(const Measurement &measurement);
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//! @brief Carries out the predict step in the predict/update cycle.
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//!
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//! Projects the state and error matrices forward using a model of
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//! the vehicle's motion.
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//!
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//! @param[in] referenceTime - The time at which the prediction is being made
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//! @param[in] delta - The time step over which to predict.
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//!
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void predict(const double referenceTime, const double delta);
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protected:
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//! @brief Computes the weighted covariance and sigma points
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//!
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void generateSigmaPoints();
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//! @brief Carries out the predict step for the posteriori state of a sigma
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//! point.
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//!
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//! Projects the state and error matrices forward using a model of
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//! the vehicle's motion.
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//!
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//! @param[in,out] sigmaPoint - The sigma point (state vector) to project
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//! @param[in] delta - The time step over which to project
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//!
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void projectSigmaPoint(Eigen::VectorXd& sigmaPoint, double delta);
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//! @brief The UKF sigma points
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//!
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//! Used to sample possible next states during prediction.
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//!
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std::vector<Eigen::VectorXd> sigmaPoints_;
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//! @brief This matrix is used to generate the sigmaPoints_
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//!
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Eigen::MatrixXd weightedCovarSqrt_;
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//! @brief The weights associated with each sigma point when generating
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//! a new state
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//!
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std::vector<double> stateWeights_;
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//! @brief The weights associated with each sigma point when calculating
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//! a predicted estimateErrorCovariance_
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//!
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std::vector<double> covarWeights_;
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//! @brief Used in weight generation for the sigma points
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//!
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double lambda_;
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//! @brief Used to determine if we need to re-compute the sigma
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//! points when carrying out multiple corrections
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//!
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bool uncorrected_;
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};
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} // namespace RobotLocalization
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#endif // ROBOT_LOCALIZATION_UKF_H
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