130 lines
3.8 KiB
C++
130 lines
3.8 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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if (H) *H = eye(3);
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return x;
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}
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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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if (H1) *H1 = -1 * eye(3);
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if (H2) *H2 = eye(3);
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return x2 - x1;
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}
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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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if (H1) *H1 = -1 * eye(3);
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if (H2) *H2 = eye(3);
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return l - x;
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}
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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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