119 lines
		
	
	
		
			3.6 KiB
		
	
	
	
		
			C++
		
	
	
			
		
		
	
	
			119 lines
		
	
	
		
			3.6 KiB
		
	
	
	
		
			C++
		
	
	
/* ----------------------------------------------------------------------------
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 * GTSAM Copyright 2010, Georgia Tech Research Corporation, 
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 * Atlanta, Georgia 30332-0415
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 * All Rights Reserved
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 * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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 * See LICENSE for the license information
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 * -------------------------------------------------------------------------- */
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/**
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 * @file   Simulated3D.h
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 * @brief  measurement functions and derivatives for simulated 3D robot
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 * @author Alex Cunningham
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 **/
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// \callgraph
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#pragma once
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#include <gtsam/base/Matrix.h>
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#include <gtsam/geometry/Point3.h>
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#include <gtsam/linear/VectorValues.h>
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#include <gtsam/nonlinear/NonlinearFactor.h>
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// \namespace
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namespace gtsam {
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namespace simulated3D {
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/**
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 * This is a linear SLAM domain where both poses and landmarks are
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 * 3D points, without rotation. The structure and use is based on
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 * the simulated2D domain.
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 */
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/**
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 * Prior on a single pose
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 */
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Point3 prior(const Point3& x, boost::optional<Matrix&> H = boost::none);
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/**
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 * odometry between two poses
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 */
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Point3 odo(const Point3& x1, const Point3& x2,
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		boost::optional<Matrix&> H1 = boost::none,
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		boost::optional<Matrix&> H2 = boost::none);
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/**
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 *  measurement between landmark and pose
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 */
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Point3 mea(const Point3& x, const Point3& l,
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		boost::optional<Matrix&> H1 = boost::none,
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		boost::optional<Matrix&> H2 = boost::none);
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/**
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 * A prior factor on a single linear robot pose
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 */
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struct PointPrior3D: public NoiseModelFactor1<Point3> {
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	Point3 measured_; ///< The prior pose value for the variable attached to this factor
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	/**
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	 * Constructor for a prior factor
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	 * @param measured is the measured/prior position for the pose
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	 * @param model is the measurement model for the factor (Dimension: 3)
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	 * @param key is the key for the pose
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	 */
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	PointPrior3D(const Point3& measured, const SharedNoiseModel& model, Key key) :
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	  NoiseModelFactor1<Point3> (model, key), measured_(measured) {
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	}
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	/**
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	 * Evaluates the error at a given value of x,
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	 * with optional derivatives.
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	 * @param x is the current value of the variable
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	 * @param H is an optional Jacobian matrix (Dimension: 3x3)
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	 * @return Vector error between prior value and x (Dimension: 3)
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	 */
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	Vector evaluateError(const Point3& x, boost::optional<Matrix&> H =
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			boost::none) const {
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		return (prior(x, H) - measured_).vector();
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	}
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};
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/**
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 * Models a linear 3D measurement between 3D points
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 */
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struct Simulated3DMeasurement: public NoiseModelFactor2<Point3, Point3> {
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	Point3 measured_; ///< Linear displacement between a pose and landmark
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	/**
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	 * Creates a measurement factor with a given measurement
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	 * @param measured is the measurement, a linear displacement between poses and landmarks
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	 * @param model is a measurement model for the factor (Dimension: 3)
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	 * @param poseKey is the pose key of the robot
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	 * @param pointKey is the point key for the landmark
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	 */
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	Simulated3DMeasurement(const Point3& measured, const SharedNoiseModel& model, Key i, Key j) :
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	      NoiseModelFactor2<Point3, Point3>(model, i, j), measured_(measured) {}
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	/**
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	 * Error function with optional derivatives
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	 * @param x1 a robot pose value
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	 * @param x2 a landmark point value
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	 * @param H1 is an optional Jacobian matrix in terms of x1 (Dimension: 3x3)
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	 * @param H2 is an optional Jacobian matrix in terms of x2 (Dimension: 3x3)
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	 * @return vector error between measurement and prediction (Dimension: 3)
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	 */
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	Vector evaluateError(const Point3& x1, const Point3& x2,
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	    boost::optional<Matrix&> H1 = boost::none, boost::optional<Matrix&> H2 = boost::none) const {
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		return (mea(x1, x2, H1, H2) - measured_).vector();
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	}
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};
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}} // namespace simulated3D
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