From f865a9e5513238132bd08fb2faa777195a53b41a Mon Sep 17 00:00:00 2001 From: Stephen Williams Date: Sun, 22 Jul 2012 16:03:42 +0000 Subject: [PATCH] Removed SLAM namespaces from Localization Example --- examples/LocalizationExample.cpp | 191 ++++++++++++++++++++++--------- 1 file changed, 135 insertions(+), 56 deletions(-) diff --git a/examples/LocalizationExample.cpp b/examples/LocalizationExample.cpp index 21fb36860..c9d232df8 100644 --- a/examples/LocalizationExample.cpp +++ b/examples/LocalizationExample.cpp @@ -15,87 +15,166 @@ * @author Frank Dellaert */ -// pull in the 2D PoseSLAM domain with all typedefs and helper functions defined -#include +/** + * A simple 2D pose slam example with "GPS" measurements + * - The robot moves forward 2 meter each iteration + * - The robot initially faces along the X axis (horizontal, to the right in 2D) + * - We have full odometry between pose + * - We have "GPS-like" measurements implemented with a custom factor + */ -// include this for marginals +// As this is a planar SLAM example, we will use Pose2 variables (x, y, theta) to represent +// the robot positions +#include + +// Each variable in the system (poses) must be identified with a unique key. +// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1). +// Here we will use simple integer keys +#include + +// In GTSAM, measurement functions are represented as 'factors'. Several common factors +// have been provided with the library for solving robotics/SLAM/Bundle Adjustment problems. +// Here we will use Between factors for the relative motion described by odometry measurements. +// Because we have global measurements in the form of "GPS-like" measurements, we don't +// actually need to provide an initial position prior in this example. We will create our +// custom factor shortly. +#include + +// When the factors are created, we will add them to a Factor Graph. As the factors we are using +// are nonlinear factors, we will need a Nonlinear Factor Graph. +#include + +// The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the +// nonlinear functions around an initial linearization point, then solve the linear system +// to update the linearization point. This happens repeatedly until the solver converges +// to a consistent set of variable values. This requires us to specify an initial guess +// for each variable, held in a Values container. +#include + +// Finally, once all of the factors have been added to our factor graph, we will want to +// solve/optimize to graph to find the best (Maximum A Posteriori) set of variable values. +// GTSAM includes several nonlinear optimizers to perform this step. Here we will use the +// standard Levenberg-Marquardt solver #include + +// Once the optimized values have been calculated, we can also calculate the marginal covariance +// of desired variables #include -#include -#include using namespace std; using namespace gtsam; -using namespace gtsam::noiseModel; -/** - * UnaryFactor - * Example on how to create a GPS-like factor on position alone. - */ + +// Before we begin the example, we must create a custom unary factor to implement a +// "GPS-like" functionality. Because standard GPS measurements provide information +// only on the position, and not on the orientation, we cannot use a simple prior to +// properly model this measurement. +// +// The factor will be a unary factor, affect only a single system variable. It will +// also use a standard Gaussian noise model. Hence, we will derive our new factor from +// the NoiseModelFactor1. +#include class UnaryFactor: public NoiseModelFactor1 { - double mx_, my_; ///< X and Y measurements + + // The factor will hold a measurement consisting of an (X,Y) location + Point2 measurement_; public: + /// shorthand for a smart pointer to a factor + typedef boost::shared_ptr shared_ptr; + + // The constructor requires the variable key, the (X, Y) measurement value, and the noise model UnaryFactor(Key j, double x, double y, const SharedNoiseModel& model): - NoiseModelFactor1(model, j), mx_(x), my_(y) {} + NoiseModelFactor1(model, j), measurement_(x, y) {} virtual ~UnaryFactor() {} - Vector evaluateError(const Pose2& q, - boost::optional H = boost::none) const + // By using the NoiseModelFactor base classes, the only two function that must be overridden. + // The first is the 'evaluateError' function. This function implements the desired measurement + // function, returning a vector of errors when evaluated at the provided variable value. It + // must also calculate the Jacobians for this measurement function, if requested. + Vector evaluateError(const Pose2& pose, boost::optional H = boost::none) const { - if (H) (*H) = Matrix_(2,3, 1.0,0.0,0.0, 0.0,1.0,0.0); - return Vector_(2, q.x() - mx_, q.y() - my_); + // The measurement function for a GPS-like measurement is simple: + // error_x = pose.x - measurement.x + // error_y = pose.y - measurement.y + // Consequently, the Jacobians are: + // [ derror_x/dx derror_x/dy derror_x/dtheta ] = [1 0 0] + // [ derror_y/dx derror_y/dy derror_y/dtheta ] = [0 1 0] + if (H) + (*H) = Matrix_(2,3, 1.0,0.0,0.0, 0.0,1.0,0.0); + + return Vector_(2, pose.x() - measurement_.x(), pose.y() - measurement_.y()); } + + // The second is a 'clone' function that allows the factor to be copied. Under most + // circumstances, the following code that employs the default copy constructor should + // work fine. + virtual gtsam::NonlinearFactor::shared_ptr clone() const { + return boost::static_pointer_cast( + gtsam::NonlinearFactor::shared_ptr(new UnaryFactor(*this))); } + + // Additionally, custom factors should really provide specific implementations of + // 'equals' to ensure proper operation will all GTSAM functionality, and a custom + // 'print' function, if desired. + virtual bool equals(const NonlinearFactor& expected, double tol=1e-9) const { + const UnaryFactor* e = dynamic_cast (&expected); + return e != NULL && NoiseModelFactor1::equals(*e, tol) && this->measurement_.equals(e->measurement_, tol); + } + + virtual void print(const std::string& s, const KeyFormatter& keyFormatter = DefaultKeyFormatter) const { + std::cout << s << "UnaryFactor(" << keyFormatter(this->key()) << ")\n"; + measurement_.print(" measurement: "); + this->noiseModel_->print(" noise model: "); + } + }; -/** - * Example of a more complex 2D localization example - * - Robot poses are facing along the X axis (horizontal, to the right in 2D) - * - The robot moves 2 meters each step - * - We have full odometry between poses - * - We have unary measurement factors at eacht time step - */ + + int main(int argc, char** argv) { - // create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph) - pose2SLAM::Graph graph; + // 1. Create a factor graph container and add factors to it + NonlinearFactorGraph graph; - // add two odometry factors - Pose2 odometry(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case) - SharedDiagonal odometryNoise = Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); // 20cm std on x,y, 0.1 rad on theta - graph.addRelativePose(1, 2, odometry, odometryNoise); - graph.addRelativePose(2, 3, odometry, odometryNoise); + // 2a. Add odometry factors + // For simplicity, we will use the same noise model for each odometry factor + noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); + // Create odometry (Between) factors between consecutive poses + graph.add(BetweenFactor(1, 2, Pose2(2.0, 0.0, 0.0), odometryNoise)); + graph.add(BetweenFactor(2, 3, Pose2(2.0, 0.0, 0.0), odometryNoise)); - // add unary measurement factors, like GPS, on all three poses - SharedDiagonal noiseModel = Diagonal::Sigmas(Vector_(2, 0.1, 0.1)); // 10cm std on x,y - graph.push_back(boost::make_shared(1, 0, 0, noiseModel)); - graph.push_back(boost::make_shared(2, 2, 0, noiseModel)); - graph.push_back(boost::make_shared(3, 4, 0, noiseModel)); + // 2b. Add "GPS-like" measurements + // We will use our custom UnaryFactor for this. + noiseModel::Diagonal::shared_ptr unaryNoise = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1)); // 10cm std on x,y + graph.add(UnaryFactor(1, 0.0, 0.0, unaryNoise)); + graph.add(UnaryFactor(3, 4.0, 0.0, unaryNoise)); + graph.print("\nFactor Graph:\n"); // print - // print - graph.print("\nFactor graph:\n"); + // 3. Create the data structure to hold the initialEstimate estimate to the solution + // For illustrative purposes, these have been deliberately set to incorrect values + Values initialEstimate; + initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2)); + initialEstimate.insert(2, Pose2(2.3, 0.1, -0.2)); + initialEstimate.insert(3, Pose2(4.1, 0.1, 0.1)); + initialEstimate.print("\nInitial Estimate:\n"); // print - // create (deliberatly inaccurate) initial estimate - pose2SLAM::Values initialEstimate; - initialEstimate.insertPose(1, Pose2(0.5, 0.0, 0.2)); - initialEstimate.insertPose(2, Pose2(2.3, 0.1,-0.2)); - initialEstimate.insertPose(3, Pose2(4.1, 0.1, 0.1)); - initialEstimate.print("\nInitial estimate:\n "); + // 4. Optimize using Levenberg-Marquardt optimization. The optimizer + // accepts an optional set of configuration parameters, controlling + // things like convergence criteria, the type of linear system solver + // to use, and the amount of information displayed during optimization. + // Here we will use the default set of parameters. See the + // documentation for the full set of parameters. + LevenbergMarquardtOptimizer optimizer(graph, initialEstimate); + Values result = optimizer.optimize(); + result.print("Final Result:\n"); - // use an explicit Optimizer object - LevenbergMarquardtOptimizer optimizer(graph, initialEstimate); - pose2SLAM::Values result = optimizer.optimize(); - result.print("\nFinal result:\n "); + // 5. Calculate and print marginal covariances for all variables + Marginals marginals(graph, result); + cout << "Pose 1 covariance:\n" << marginals.marginalCovariance(1) << endl; + cout << "Pose 2 covariance:\n" << marginals.marginalCovariance(2) << endl; + cout << "Pose 3 covariance:\n" << marginals.marginalCovariance(3) << endl; - // Query the marginals - Marginals marginals(graph, result); - cout.precision(2); - cout << "\nP1:\n" << marginals.marginalCovariance(1) << endl; - cout << "\nP2:\n" << marginals.marginalCovariance(2) << endl; - cout << "\nP3:\n" << marginals.marginalCovariance(3) << endl; - - return 0; + return 0; } -