127 lines
4.9 KiB
C++
127 lines
4.9 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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/**
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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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// In this example, a 2D rotation will be used as the variable of interest
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#include <gtsam/geometry/Rot2.h>
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// Each variable in the system (poses) must be identified with a unique key.
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// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
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// Here we will use symbols
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#include <gtsam/inference/Symbol.h>
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// In GTSAM, measurement functions are represented as 'factors'. Several common factors
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// have been provided with the library for solving robotics/SLAM/Bundle Adjustment problems.
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// We will apply a simple prior on the rotation. We do so via the `addPrior` convenience
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// method in NonlinearFactorGraph.
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// When the factors are created, we will add them to a Factor Graph. As the factors we are using
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// are nonlinear factors, we will need a Nonlinear Factor Graph.
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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// The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the
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// nonlinear functions around an initial linearization point, then solve the linear system
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// to update the linearization point. This happens repeatedly until the solver converges
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// to a consistent set of variable values. This requires us to specify an initial guess
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// for each variable, held in a Values container.
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#include <gtsam/nonlinear/Values.h>
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// Finally, once all of the factors have been added to our factor graph, we will want to
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// solve/optimize to graph to find the best (Maximum A Posteriori) set of variable values.
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// GTSAM includes several nonlinear optimizers to perform this step. Here we will use the
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// standard Levenberg-Marquardt solver
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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using namespace std;
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using namespace gtsam;
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const double degree = M_PI / 180;
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int main() {
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/**
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* Step 1: Create a factor 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 "RotValues") 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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auto model = noiseModel::Isotropic::Sigma(1, 1 * degree);
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Symbol key('x', 1);
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/**
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* Step 2: 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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NonlinearFactorGraph graph;
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graph.addPrior(key, prior, model);
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graph.print("full graph");
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/**
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* Step 3: 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 "RotValues" 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 RotValues 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 4: 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 RotValues structure
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* with the final state of the optimization.
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*/
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Values result = LevenbergMarquardtOptimizer(graph, initial).optimize();
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result.print("final result");
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return 0;
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}
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