134 lines
4.7 KiB
C++
134 lines
4.7 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 SimpleRotation.cpp
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* @brief This is a super-simple example of optimizing a single rotation according to a single prior
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* @date Jul 1, 2010
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* @author Frank Dellaert
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* @author Alex Cunningham
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*/
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#include <cmath>
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#include <iostream>
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/geometry/Rot2.h>
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#include <gtsam/linear/NoiseModel.h>
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#include <gtsam/nonlinear/Key.h>
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#include <gtsam/nonlinear/LieValues-inl.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph-inl.h>
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#include <gtsam/nonlinear/NonlinearOptimization-inl.h>
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/*
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* TODO: make factors independent of Values
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* TODO: make toplevel documentation
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* TODO: Clean up nonlinear optimization API
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*/
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using namespace std;
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using namespace gtsam;
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/**
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* Step 1: Setup basic types for optimization of a single variable type
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* This can be considered to be specifying the domain of the problem we wish
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* to solve. In this case, we will create a very simple domain that operates
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* on variables of a specific type, in this case, Rot2.
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*
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* To create a domain:
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* - variable types need to have a key defined to act as a label in graphs
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* - a "Values" structure needs to be defined to store the system state
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* - a graph container acting on a given Values
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*
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* In a typical scenario, these typedefs could be placed in a header
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* file and reused between projects. Also, LieValues can be combined to
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* form a "TupleValues" to enable multiple variable types, such as 2D points
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* and 2D poses.
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*/
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typedef TypedSymbol<Rot2, 'x'> Key; /// Variable labels for a specific type
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typedef LieValues<Key> Values; /// Class to store values - acts as a state for the system
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typedef NonlinearFactorGraph<Values> Graph; /// Graph container for constraints - needs to know type of variables
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const double degree = M_PI / 180;
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int main() {
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/**
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* This example will perform a relatively trivial optimization on
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* a single variable with a single factor.
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*/
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/**
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* Step 2: create a factor on to express a unary constraint
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* The "prior" in this case is the measurement from a sensor,
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* with a model of the noise on the measurement.
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*
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* The "Key" created here is a label used to associate parts of the
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* state (stored in "Values") with particular factors. They require
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* an index to allow for lookup, and should be unique.
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*
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* In general, creating a factor requires:
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* - A key or set of keys labeling the variables that are acted upon
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* - A measurement value
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* - A measurement model with the correct dimensionality for the factor
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*/
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Rot2 prior = Rot2::fromAngle(30 * degree);
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prior.print("goal angle");
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SharedDiagonal model = noiseModel::Isotropic::Sigma(1, 1 * degree);
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Key key(1);
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PriorFactor<Values, Key> factor(key, prior, model);
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/**
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* Step 3: create a graph container and add the factor to it
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* Before optimizing, all factors need to be added to a Graph container,
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* which provides the necessary top-level functionality for defining a
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* system of constraints.
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*
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* In this case, there is only one factor, but in a practical scenario,
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* many more factors would be added.
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*/
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Graph graph;
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graph.add(factor);
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graph.print("full graph");
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/**
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* Step 4: create an initial estimate
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* An initial estimate of the solution for the system is necessary to
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* start optimization. This system state is the "Values" structure,
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* which is similar in structure to a STL map, in that it maps
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* keys (the label created in step 1) to specific values.
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*
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* The initial estimate provided to optimization will be used as
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* a linearization point for optimization, so it is important that
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* all of the variables in the graph have a corresponding value in
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* this structure.
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*
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* The interface to all Values types is the same, it only depends
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* on the type of key used to find the appropriate value map if there
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* are multiple types of variables.
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*/
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Values initial;
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initial.insert(key, Rot2::fromAngle(20 * degree));
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initial.print("initial estimate");
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/**
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* Step 5: optimize
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* After formulating the problem with a graph of constraints
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* and an initial estimate, executing optimization is as simple
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* as calling a general optimization function with the graph and
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* initial estimate. This will yield a new Values structure
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* with the final state of the optimization.
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*/
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Values result = optimize<Graph, Values>(graph, initial);
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result.print("final result");
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return 0;
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}
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