Modified Vector_() to Vec() in examples
Jing Dong
parent
56e1fb5af0
commit
4478bc6b87
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@ -70,7 +70,7 @@ int main(int argc, char* argv[]) {
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/* 2. add factors to the graph */
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// add measurement factors
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SharedDiagonal measurementNoise = Diagonal::Sigmas(Vector_(2, 0.5, 0.5));
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SharedDiagonal measurementNoise = Diagonal::Sigmas((Vec(2) << 0.5, 0.5));
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boost::shared_ptr<ResectioningFactor> factor;
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graph.push_back(
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boost::make_shared<ResectioningFactor>(measurementNoise, X(1), calib,
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@ -94,7 +94,7 @@ public:
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// [ derror_x/dx derror_x/dy derror_x/dtheta ] = [1 0 0]
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// [ derror_y/dx derror_y/dy derror_y/dtheta ] = [0 1 0]
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if (H) (*H) = Matrix_(2,3, 1.0,0.0,0.0, 0.0,1.0,0.0);
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return Vector_(2, q.x() - mx_, q.y() - my_);
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return (Vec(2) << q.x() - mx_, q.y() - my_);
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}
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// The second is a 'clone' function that allows the factor to be copied. Under most
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@ -118,14 +118,14 @@ int main(int argc, char** argv) {
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// 2a. Add odometry factors
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// For simplicity, we will use the same noise model for each odometry factor
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noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
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noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas((Vec(3) << 0.2, 0.2, 0.1));
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// Create odometry (Between) factors between consecutive poses
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graph.push_back(BetweenFactor<Pose2>(1, 2, Pose2(2.0, 0.0, 0.0), odometryNoise));
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graph.push_back(BetweenFactor<Pose2>(2, 3, Pose2(2.0, 0.0, 0.0), odometryNoise));
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// 2b. Add "GPS-like" measurements
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// We will use our custom UnaryFactor for this.
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noiseModel::Diagonal::shared_ptr unaryNoise = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1)); // 10cm std on x,y
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noiseModel::Diagonal::shared_ptr unaryNoise = noiseModel::Diagonal::Sigmas((Vec(2) << 0.1, 0.1)); // 10cm std on x,y
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graph.push_back(boost::make_shared<UnaryFactor>(1, 0.0, 0.0, unaryNoise));
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graph.push_back(boost::make_shared<UnaryFactor>(2, 2.0, 0.0, unaryNoise));
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graph.push_back(boost::make_shared<UnaryFactor>(3, 4.0, 0.0, unaryNoise));
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@ -64,13 +64,13 @@ int main(int argc, char** argv) {
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// Add a prior on the first pose, setting it to the origin
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// A prior factor consists of a mean and a noise model (covariance matrix)
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Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas((Vec(3) << 0.3, 0.3, 0.1));
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graph.push_back(PriorFactor<Pose2>(1, priorMean, priorNoise));
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// Add odometry factors
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Pose2 odometry(2.0, 0.0, 0.0);
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// For simplicity, we will use the same noise model for each odometry factor
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noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
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noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas((Vec(3) << 0.2, 0.2, 0.1));
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// Create odometry (Between) factors between consecutive poses
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graph.push_back(BetweenFactor<Pose2>(1, 2, odometry, odometryNoise));
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graph.push_back(BetweenFactor<Pose2>(2, 3, odometry, odometryNoise));
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@ -80,18 +80,18 @@ int main(int argc, char** argv) {
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// Add a prior on pose x1 at the origin. A prior factor consists of a mean and a noise model (covariance matrix)
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Pose2 prior(0.0, 0.0, 0.0); // prior mean is at origin
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1)); // 30cm std on x,y, 0.1 rad on theta
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas((Vec(3) << 0.3, 0.3, 0.1)); // 30cm std on x,y, 0.1 rad on theta
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graph.push_back(PriorFactor<Pose2>(x1, prior, priorNoise)); // add directly to graph
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// Add two odometry factors
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Pose2 odometry(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case)
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noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); // 20cm std on x,y, 0.1 rad on theta
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noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas((Vec(3) << 0.2, 0.2, 0.1)); // 20cm std on x,y, 0.1 rad on theta
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graph.push_back(BetweenFactor<Pose2>(x1, x2, odometry, odometryNoise));
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graph.push_back(BetweenFactor<Pose2>(x2, x3, odometry, odometryNoise));
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// Add Range-Bearing measurements to two different landmarks
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// create a noise model for the landmark measurements
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noiseModel::Diagonal::shared_ptr measurementNoise = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.2)); // 0.1 rad std on bearing, 20cm on range
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noiseModel::Diagonal::shared_ptr measurementNoise = noiseModel::Diagonal::Sigmas((Vec(2) << 0.1, 0.2)); // 0.1 rad std on bearing, 20cm on range
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// create the measurement values - indices are (pose id, landmark id)
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Rot2 bearing11 = Rot2::fromDegrees(45),
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bearing21 = Rot2::fromDegrees(90),
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@ -71,11 +71,11 @@ int main(int argc, char** argv) {
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// 2a. Add a prior on the first pose, setting it to the origin
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// A prior factor consists of a mean and a noise model (covariance matrix)
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas((Vec(3) << 0.3, 0.3, 0.1));
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graph.push_back(PriorFactor<Pose2>(1, Pose2(0, 0, 0), priorNoise));
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// For simplicity, we will use the same noise model for odometry and loop closures
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noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
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noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas((Vec(3) << 0.2, 0.2, 0.1));
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// 2b. Add odometry factors
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// Create odometry (Between) factors between consecutive poses
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@ -31,13 +31,13 @@ int main (int argc, char** argv) {
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// we are in build/examples, data is in examples/Data
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NonlinearFactorGraph::shared_ptr graph;
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Values::shared_ptr initial;
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SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.05, 0.05, 5.0 * M_PI / 180.0));
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SharedDiagonal model = noiseModel::Diagonal::Sigmas((Vec(3) << 0.05, 0.05, 5.0 * M_PI / 180.0));
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boost::tie(graph, initial) = load2D("../../examples/Data/w100.graph", model);
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initial->print("Initial estimate:\n");
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// Add a Gaussian prior on first poses
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Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
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SharedDiagonal priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.01, 0.01, 0.01));
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SharedDiagonal priorNoise = noiseModel::Diagonal::Sigmas((Vec(3) << 0.01, 0.01, 0.01));
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graph->push_back(PriorFactor<Pose2>(0, priorMean, priorNoise));
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// Single Step Optimization using Levenberg-Marquardt
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@ -32,11 +32,11 @@ int main (int argc, char** argv) {
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NonlinearFactorGraph graph;
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// 2a. Add a prior on the first pose, setting it to the origin
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas((Vec(3) << 0.3, 0.3, 0.1));
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graph.push_back(PriorFactor<Pose2>(1, Pose2(0, 0, 0), priorNoise));
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// For simplicity, we will use the same noise model for odometry and loop closures
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noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
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noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas((Vec(3) << 0.2, 0.2, 0.1));
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// 2b. Add odometry factors
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graph.push_back(BetweenFactor<Pose2>(1, 2, Pose2(2, 0, 0 ), model));
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@ -68,12 +68,12 @@ int main(int argc, char** argv) {
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// 2a. Add a prior on the first pose, setting it to the origin
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// A prior factor consists of a mean and a noise model (covariance matrix)
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Pose2 prior(0.0, 0.0, 0.0); // prior at origin
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
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noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas((Vec(3) << 0.3, 0.3, 0.1));
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graph.push_back(PriorFactor<Pose2>(1, prior, priorNoise));
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// 2b. Add odometry factors
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// For simplicity, we will use the same noise model for each odometry factor
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noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
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noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas((Vec(3) << 0.2, 0.2, 0.1));
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// Create odometry (Between) factors between consecutive poses
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graph.push_back(BetweenFactor<Pose2>(1, 2, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
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graph.push_back(BetweenFactor<Pose2>(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
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@ -85,7 +85,7 @@ int main(int argc, char** argv) {
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// these constraints may be identified in many ways, such as appearance-based techniques
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// with camera images.
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// We will use another Between Factor to enforce this constraint, with the distance set to zero,
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noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
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noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas((Vec(3) << 0.2, 0.2, 0.1));
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graph.push_back(BetweenFactor<Pose2>(5, 1, Pose2(0.0, 0.0, 0.0), model));
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graph.print("\nFactor Graph:\n"); // print
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@ -53,7 +53,7 @@ int main(int argc, char* argv[]) {
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NonlinearFactorGraph graph;
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// Add a prior on pose x1.
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noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas(Vector_(6, 0.3, 0.3, 0.3, 0.1, 0.1, 0.1)); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas((Vec(6) << Vector3::Constant(0.3), Vector3::Constant(0.1))); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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graph.push_back(PriorFactor<Pose3>(Symbol('x', 0), poses[0], poseNoise));
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// Simulated measurements from each camera pose, adding them to the factor graph
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@ -74,7 +74,7 @@ int main(int argc, char* argv[]) {
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graph.push_back(PriorFactor<Point3>(Symbol('l', 0), points[0], pointNoise)); // add directly to graph
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// Add a prior on the calibration.
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noiseModel::Diagonal::shared_ptr calNoise = noiseModel::Diagonal::Sigmas(Vector_(5, 500, 500, 0.1, 100, 100));
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noiseModel::Diagonal::shared_ptr calNoise = noiseModel::Diagonal::Sigmas((Vec(5) << 500, 500, 0.1, 100, 100));
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graph.push_back(PriorFactor<Cal3_S2>(Symbol('K', 0), K, calNoise));
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// Create the initial estimate to the solution
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@ -103,7 +103,7 @@ int main(int argc, char* argv[]) {
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// adding it to iSAM.
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if( i == 0) {
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// Add a prior on pose x0
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noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas(Vector_(6, 0.3, 0.3, 0.3, 0.1, 0.1, 0.1)); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas((Vec(6) << Vector3::Constant(0.3),Vector3::Constant(0.1))); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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graph.push_back(PriorFactor<Pose3>(Symbol('x', 0), poses[0], poseNoise));
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// Add a prior on landmark l0
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@ -97,7 +97,7 @@ int main(int argc, char* argv[]) {
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// adding it to iSAM.
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if( i == 0) {
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// Add a prior on pose x0
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noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas(Vector_(6, 0.3, 0.3, 0.3, 0.1, 0.1, 0.1)); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas((Vec(6) << Vector3::Constant(0.3), Vector3::Constant(0.1))); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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graph.push_back(PriorFactor<Pose3>(Symbol('x', 0), poses[0], poseNoise));
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// Add a prior on landmark l0
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@ -80,7 +80,7 @@ int main(int argc, char* argv[]) {
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NonlinearFactorGraph graph;
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// Add a prior on pose x1. This indirectly specifies where the origin is.
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noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas(Vector_(6, 0.3, 0.3, 0.3, 0.1, 0.1, 0.1)); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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noiseModel::Diagonal::shared_ptr poseNoise = noiseModel::Diagonal::Sigmas((Vec(6) << Vector3::Constant(0.3), Vector3::Constant(0.1))); // 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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graph.push_back(PriorFactor<Pose3>(Symbol('x', 0), poses[0], poseNoise)); // add directly to graph
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// Simulated measurements from each camera pose, adding them to the factor graph
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@ -37,7 +37,7 @@ int main() {
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// Create the Kalman Filter initialization point
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Point2 x_initial(0.0, 0.0);
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SharedDiagonal P_initial = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1));
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SharedDiagonal P_initial = noiseModel::Diagonal::Sigmas((Vec(2) << 0.1, 0.1));
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// Create Key for initial pose
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Symbol x0('x',0);
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@ -57,8 +57,8 @@ int main() {
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// For the purposes of this example, let us assume we are using a constant-position model and
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// the controls are driving the point to the right at 1 m/s. Then, F = [1 0 ; 0 1], B = [1 0 ; 0 1]
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// and u = [1 ; 0]. Let us also assume that the process noise Q = [0.1 0 ; 0 0.1].
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Vector u = Vector_(2, 1.0, 0.0);
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SharedDiagonal Q = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1), true);
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Vector u = (Vec(2) << 1.0, 0.0);
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SharedDiagonal Q = noiseModel::Diagonal::Sigmas((Vec(2) << 0.1, 0.1), true);
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// This simple motion can be modeled with a BetweenFactor
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// Create Key for next pose
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@ -83,7 +83,7 @@ int main() {
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// For the purposes of this example, let us assume we have something like a GPS that returns
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// the current position of the robot. Then H = [1 0 ; 0 1]. Let us also assume that the measurement noise
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// R = [0.25 0 ; 0 0.25].
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SharedDiagonal R = noiseModel::Diagonal::Sigmas(Vector_(2, 0.25, 0.25), true);
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SharedDiagonal R = noiseModel::Diagonal::Sigmas((Vec(2) << 0.25, 0.25), true);
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// This simple measurement can be modeled with a PriorFactor
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Point2 z1(1.0, 0.0);
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@ -58,7 +58,7 @@ int main() {
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// Initialize state x0 (2D point) at origin by adding a prior factor, i.e., Bayes net P(x0)
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// This is equivalent to x_0 and P_0
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Point2 x_initial(0,0);
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SharedDiagonal P_initial = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1));
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SharedDiagonal P_initial = noiseModel::Diagonal::Sigmas((Vec(2) << 0.1, 0.1));
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PriorFactor<Point2> factor1(x0, x_initial, P_initial);
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// Linearize the factor and add it to the linear factor graph
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linearizationPoints.insert(x0, x_initial);
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@ -89,7 +89,7 @@ int main() {
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ordering->insert(x1, 1);
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Point2 difference(1,0);
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SharedDiagonal Q = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1));
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SharedDiagonal Q = noiseModel::Diagonal::Sigmas((Vec(2) << 0.1, 0.1));
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BetweenFactor<Point2> factor2(x0, x1, difference, Q);
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// Linearize the factor and add it to the linear factor graph
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linearizationPoints.insert(x1, x_initial);
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@ -167,7 +167,7 @@ int main() {
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// = (x_{t} - z_{t}) * R^-1 * (x_{t} - z_{t})^T
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// This can be modeled using the PriorFactor, where the mean is z_{t} and the covariance is R.
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Point2 z1(1.0, 0.0);
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SharedDiagonal R1 = noiseModel::Diagonal::Sigmas(Vector_(2, 0.25, 0.25));
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SharedDiagonal R1 = noiseModel::Diagonal::Sigmas((Vec(2) << 0.25, 0.25));
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PriorFactor<Point2> factor4(x1, z1, R1);
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// Linearize the factor and add it to the linear factor graph
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linearFactorGraph->push_back(factor4.linearize(linearizationPoints, *ordering));
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// Create a nonlinear factor describing the motion model
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difference = Point2(1,0);
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Q = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1));
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Q = noiseModel::Diagonal::Sigmas((Vec(2) <, 0.1, 0.1));
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BetweenFactor<Point2> factor6(x1, x2, difference, Q);
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// Linearize the factor and add it to the linear factor graph
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@ -257,7 +257,7 @@ int main() {
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// And update using z2 ...
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Point2 z2(2.0, 0.0);
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SharedDiagonal R2 = noiseModel::Diagonal::Sigmas(Vector_(2, 0.25, 0.25));
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SharedDiagonal R2 = noiseModel::Diagonal::Sigmas((Vec(2) << 0.25, 0.25));
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PriorFactor<Point2> factor8(x2, z2, R2);
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// Linearize the factor and add it to the linear factor graph
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@ -308,7 +308,7 @@ int main() {
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// Create a nonlinear factor describing the motion model
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difference = Point2(1,0);
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Q = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1));
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Q = noiseModel::Diagonal::Sigmas((Vec(2) << 0.1, 0.1));
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BetweenFactor<Point2> factor10(x2, x3, difference, Q);
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// Linearize the factor and add it to the linear factor graph
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@ -346,7 +346,7 @@ int main() {
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// And update using z3 ...
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Point2 z3(3.0, 0.0);
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SharedDiagonal R3 = noiseModel::Diagonal::Sigmas(Vector_(2, 0.25, 0.25));
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SharedDiagonal R3 = noiseModel::Diagonal::Sigmas((Vec(2) << 0.25, 0.25));
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PriorFactor<Point2> factor12(x3, z3, R3);
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// Linearize the factor and add it to the linear factor graph
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|
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Loading…
Reference in New Issue