remove noise sampler in visualSLAM examples
parent
8037c44b17
commit
a8ffa407ae
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@ -53,10 +53,8 @@ int main(int argc, char* argv[]) {
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// First pose with prior factor
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newFactors.addPosePrior(X(0), data.poses[0], data.noiseX);
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// Second pose with odometry measurement, simulated by adding Gaussian noise to the ground-truth.
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Pose3 odoMeasurement = data.odometry*Pose3::Expmap(data.noiseX->sample());
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newFactors.push_back( boost::shared_ptr<BetweenFactor<Pose3> >(
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new BetweenFactor<Pose3>(X(0), X(1), odoMeasurement, data.noiseX)));
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// Second pose with odometry measurement
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newFactors.addOdometry(X(0), X(1), data.odometry, data.noiseX);
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// Visual measurements at both poses
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for (size_t i=0; i<2; ++i) {
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@ -67,13 +65,12 @@ int main(int argc, char* argv[]) {
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// Initial values for the first two poses, simulated with Gaussian noise
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Values initials;
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Pose3 pose0Init = data.poses[0]*Pose3::Expmap(data.noiseX->sample());
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initials.insert(X(0), pose0Init);
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initials.insert(X(1), pose0Init*odoMeasurement);
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initials.insert(X(0), data.poses[0]);
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initials.insert(X(1), data.poses[0]*data.odometry);
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// Initial values for the landmarks, simulated with Gaussian noise
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// Initial values for the landmarks
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for (size_t j=0; j<data.points.size(); ++j)
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initials.insert(L(j), data.points[j] + Point3(data.noiseL->sample()));
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initials.insert(L(j), data.points[j]);
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// Update ISAM the first time and obtain the current estimate
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isam.update(newFactors, initials);
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@ -87,9 +84,8 @@ int main(int argc, char* argv[]) {
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for (size_t i=2; i<data.poses.size(); ++i) {
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visualSLAM::Graph newFactors;
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// Factor for odometry measurements, simulated by adding Gaussian noise to the ground-truth.
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Pose3 odoMeasurement = data.odometry*Pose3::Expmap(data.noiseX->sample());
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newFactors.push_back( boost::shared_ptr<BetweenFactor<Pose3> >(
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new BetweenFactor<Pose3>(X(i-1), X(i), odoMeasurement, data.noiseX)));
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Pose3 odoMeasurement = data.odometry;
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newFactors.addOdometry(X(i-1), X(i), data.odometry, data.noiseX);
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// Factors for visual measurements
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for (size_t j=0; j<data.z[i].size(); ++j) {
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newFactors.addMeasurement(data.z[i][j], data.noiseZ, X(i), L(j), data.sK);
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@ -97,7 +93,7 @@ int main(int argc, char* argv[]) {
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// Initial estimates for the new node Xi, simulated by Gaussian noises
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Values initials;
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initials.insert(X(i), currentEstimate.at<Pose3>(X(i-1))*odoMeasurement);
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initials.insert(X(i), currentEstimate.at<Pose3>(X(i-1))*data.odometry);
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// update ISAM
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isam.update(newFactors, initials);
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@ -66,20 +66,19 @@ struct VisualSLAMExampleData {
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double theta = 0.0;
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double r = 30.0;
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for (int i=0; i<n; ++i, theta += 2*M_PI/n) {
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data.poses.push_back(gtsam::Pose3(
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gtsam::Rot3(-sin(theta), 0.0, -cos(theta),
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cos(theta), 0.0, -sin(theta),
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0.0, -1.0, 0.0),
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gtsam::Point3(r*cos(theta), r*sin(theta), 0.0)));
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Point3 C = gtsam::Point3(r*cos(theta), r*sin(theta), 0.0);
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SimpleCamera camera = SimpleCamera::lookat(C, Point3(), Point3(0,0,1));
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data.poses.push_back(camera.pose());
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}
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data.odometry = data.poses[0].between(data.poses[1]);
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// Simulated measurements with Gaussian noise
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// Simulated measurements, possibly with Gaussian noise
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data.noiseZ = gtsam::sharedSigma(2, 1.0);
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for (size_t i=0; i<data.poses.size(); ++i) {
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for (size_t j=0; j<data.points.size(); ++j) {
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gtsam::SimpleCamera camera(data.poses[i], *data.sK);
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data.z[i].push_back(camera.project(data.points[j]) + gtsam::Point2(data.noiseZ->sample()));
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data.z[i].push_back(camera.project(data.points[j])
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/*+ gtsam::Point2(data.noiseZ->sample()))*/); // you can add noise as desired
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}
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}
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data.noiseX = gtsam::sharedSigmas(gtsam::Vector_(6, 0.001, 0.001, 0.001, 0.1, 0.1, 0.1));
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@ -49,9 +49,9 @@ int main(int argc, char* argv[]) {
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/* 3. Initial estimates for variable nodes, simulated by Gaussian noises */
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Values initial;
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for (size_t i=0; i<data.poses.size(); ++i)
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initial.insert(X(i), data.poses[i]*Pose3::Expmap(data.noiseX->sample()));
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initial.insert(X(i), data.poses[i]/* *Pose3::Expmap(data.noiseX->sample())*/); // you can add noise if you want
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for (size_t j=0; j<data.points.size(); ++j)
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initial.insert(L(j), data.points[j] + Point3(data.noiseL->sample()));
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initial.insert(L(j), data.points[j] /*+ Point3(data.noiseL->sample())*/); // you can add noise if you want
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initial.print("Intial Estimates: ");
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/* 4. Optimize the graph and print results */
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