diff --git a/examples/LocalizationExample.cpp b/examples/LocalizationExample.cpp index c9d232df8..dfe3d1e99 100644 --- a/examples/LocalizationExample.cpp +++ b/examples/LocalizationExample.cpp @@ -23,25 +23,16 @@ * - We have "GPS-like" measurements implemented with a custom factor */ -// As this is a planar SLAM example, we will use Pose2 variables (x, y, theta) to represent -// the robot positions +// 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 +// We will use simple integer Keys to refer to the robot poses. #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. +// As in OdometryExample.cpp, we use a BetweenFactor to model odometry measurements. #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. +// We add all facors to a Nonlinear Factor Graph, as our factors are nonlinear. #include // The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the @@ -61,11 +52,9 @@ // of desired variables #include - using namespace std; using namespace gtsam; - // 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 @@ -75,10 +64,12 @@ using namespace gtsam; // also use a standard Gaussian noise model. Hence, we will derive our new factor from // the NoiseModelFactor1. #include + class UnaryFactor: public NoiseModelFactor1 { // The factor will hold a measurement consisting of an (X,Y) location - Point2 measurement_; + // We could this with a Point2 but here we just use two doubles + double mx_, my_; public: /// shorthand for a smart pointer to a factor @@ -86,15 +77,15 @@ public: // 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), measurement_(x, y) {} + NoiseModelFactor1(model, j), mx_(x), my_(y) {} virtual ~UnaryFactor() {} - // By using the NoiseModelFactor base classes, the only two function that must be overridden. + // Using the NoiseModelFactor1 base class there are two functions 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 + Vector evaluateError(const Pose2& q, boost::optional H = boost::none) const { // The measurement function for a GPS-like measurement is simple: // error_x = pose.x - measurement.x @@ -102,10 +93,8 @@ public: // 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()); + 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 second is a 'clone' function that allows the factor to be copied. Under most @@ -115,22 +104,11 @@ public: 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: "); - } - -}; + // Additionally, we encourage you the use of unit testing your custom factors, + // (as all GTSAM factors are), in which you would need an equals and print, to satisfy the + // GTSAM_CONCEPT_TESTABLE_INST(T) defined in Testable.h, but these are not needed below. +}; // UnaryFactor int main(int argc, char** argv) { @@ -148,8 +126,9 @@ int main(int argc, char** argv) { // 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.push_back(boost::make_shared(1, 0.0, 0.0, unaryNoise)); + graph.push_back(boost::make_shared(2, 2.0, 0.0, unaryNoise)); + graph.push_back(boost::make_shared(3, 4.0, 0.0, unaryNoise)); graph.print("\nFactor Graph:\n"); // print // 3. Create the data structure to hold the initialEstimate estimate to the solution @@ -172,9 +151,9 @@ int main(int argc, char** argv) { // 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; + cout << "x1 covariance:\n" << marginals.marginalCovariance(1) << endl; + cout << "x2 covariance:\n" << marginals.marginalCovariance(2) << endl; + cout << "x3 covariance:\n" << marginals.marginalCovariance(3) << endl; return 0; } diff --git a/examples/OdometryExample.cpp b/examples/OdometryExample.cpp index 7c2a80d85..d556798f0 100644 --- a/examples/OdometryExample.cpp +++ b/examples/OdometryExample.cpp @@ -22,10 +22,8 @@ * - We have full odometry between poses */ -// As this is a planar SLAM example, we will use Pose2 variables (x, y, theta) to represent -// the robot positions +// We will use Pose2 variables (x, y, theta) to represent the robot positions #include -#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.