Updated the VisualSLAM examples, removing the SLAM namespaces
Stephen Williams
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/**
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/**
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* @file VisualISAMExample.cpp
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* @file VisualISAMExample.cpp
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* @brief An ISAM example for synthesis sequence, single camera
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* @brief A visualSLAM example for the structure-from-motion problem on a simulated dataset
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* This version uses iSAM to solve the problem incrementally
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* @author Duy-Nguyen Ta
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* @author Duy-Nguyen Ta
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*/
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*/
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/**
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* A structure-from-motion example with landmarks
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* - The landmarks form a 10 meter cube
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* - The robot rotates around the landmarks, always facing towards the cube
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*/
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// As this is a full 3D problem, we will use Pose3 variables to represent the camera
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// positions and Point3 variables (x, y, z) to represent the landmark coordinates.
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// Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).
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// We will also need a camera object to hold calibration information and perform projections.
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#include <gtsam/geometry/Pose3.h>
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#include <gtsam/geometry/Point3.h>
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#include <gtsam/geometry/Point2.h>
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#include <gtsam/geometry/SimpleCamera.h>
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// Each variable in the system (poses and landmarks) 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/nonlinear/Symbol.h>
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#include <gtsam/nonlinear/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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// Here we will use Projection factors to model the camera's landmark observations.
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// Also, we will initialize the robot at some location using a Prior factor.
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/ProjectionFactor.h>
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// We want to use iSAM to solve the structure-from-motion problem incrementally, so
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// include iSAM here
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#include <gtsam/nonlinear/NonlinearISAM.h>
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#include <gtsam/nonlinear/NonlinearISAM.h>
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#include <gtsam/slam/visualSLAM.h>
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#include <gtsam/slam/BetweenFactor.h>
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// iSAM requires as input a set set of new factors to be added stored in a factor graph,
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#include "VisualSLAMData.h"
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// and initial guesses for any new variables used in the added factors
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/Values.h>
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#include <vector>
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using namespace std;
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using namespace std;
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using namespace gtsam;
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using namespace gtsam;
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// Convenience for named keys
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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/* ************************************************************************* */
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/* ************************************************************************* */
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int main(int argc, char* argv[]) {
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int main(int argc, char* argv[]) {
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VisualSLAMExampleData data = VisualSLAMExampleData::generate();
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// Define the camera calibration parameters
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Cal3_S2::shared_ptr K(new Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0));
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/* 1. Create a NonlinearISAM which will be relinearized and reordered after every "relinearizeInterval" updates */
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// Define the camera observation noise model
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int relinearizeInterval = 3;
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noiseModel::Isotropic::shared_ptr measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v
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NonlinearISAM isam(relinearizeInterval);
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/* 2. At each frame (poseId) with new camera pose and set of associated measurements,
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// Create the set of ground-truth landmarks
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* create a graph of new factors and update ISAM */
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std::vector<gtsam::Point3> points;
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points.push_back(gtsam::Point3(10.0,10.0,10.0));
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points.push_back(gtsam::Point3(-10.0,10.0,10.0));
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points.push_back(gtsam::Point3(-10.0,-10.0,10.0));
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points.push_back(gtsam::Point3(10.0,-10.0,10.0));
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points.push_back(gtsam::Point3(10.0,10.0,-10.0));
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points.push_back(gtsam::Point3(-10.0,10.0,-10.0));
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points.push_back(gtsam::Point3(-10.0,-10.0,-10.0));
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points.push_back(gtsam::Point3(10.0,-10.0,-10.0));
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// Store the current best estimate from ISAM
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// Create the set of ground-truth poses
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Values currentEstimate;
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std::vector<gtsam::Pose3> poses;
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double radius = 30.0;
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// First two frames:
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int i = 0;
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// Add factors and initial values for the first two poses and landmarks then update ISAM.
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double theta = 0.0;
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// Note: measurements from the first pose only are not enough to update ISAM:
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gtsam::Point3 up(0,0,1);
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// the system is underconstrained.
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gtsam::Point3 target(0,0,0);
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{
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for(; i < 8; ++i, theta += 2*M_PI/8) {
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visualSLAM::Graph newFactors;
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gtsam::Point3 position = Point3(radius*cos(theta), radius*sin(theta), 0.0);
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gtsam::SimpleCamera camera = SimpleCamera::Lookat(position, target, up);
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// First pose with prior factor
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poses.push_back(camera.pose());
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newFactors.addPosePrior(X(0), data.poses[0], data.noiseX);
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// Second pose with odometry measurement
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newFactors.addRelativePose(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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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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}
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}
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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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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
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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]);
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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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currentEstimate = isam.estimate();
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cout << "Frame 0 and 1: " << endl;
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currentEstimate.print("Current estimate: ");
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}
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}
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// Subsequent frames: Add new odometry and measurement factors and initial values,
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// Create a NonlinearISAM object which will relinearize and reorder the variables every "relinearizeInterval" updates
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// then update ISAM at each frame
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int relinearizeInterval = 1;
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for (size_t i=2; i<data.poses.size(); ++i) {
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NonlinearISAM isam(relinearizeInterval);
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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;
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newFactors.addRelativePose(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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}
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// Initial estimates for the new node Xi, simulated by Gaussian noises
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// Create a Factor Graph and Values to hold the new data
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Values initials;
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NonlinearFactorGraph graph;
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initials.insert(X(i), currentEstimate.at<Pose3>(X(i-1))*data.odometry);
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Values initialEstimate;
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// update ISAM
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// Loop over the different poses, adding the observations to iSAM incrementally
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isam.update(newFactors, initials);
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for (size_t i = 0; i < poses.size(); ++i) {
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currentEstimate = isam.estimate();
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cout << "****************************************************" << endl;
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// Add factors for each landmark observation
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cout << "Frame " << i << ": " << endl;
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for (size_t j = 0; j < points.size(); ++j) {
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currentEstimate.print("Current estimate: ");
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SimpleCamera camera(poses[i], *K);
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Point2 measurement = camera.project(points[j]);
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graph.add(GenericProjectionFactor<Pose3, Point3, Cal3_S2>(measurement, measurementNoise, Symbol('x', i), Symbol('l', j), K));
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}
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// Add an initial guess for the current pose
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// Intentionally initialize the variables off from the ground truth
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initialEstimate.insert(Symbol('x', i), poses[i].compose(Pose3(Rot3::rodriguez(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20))));
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// If this is the first iteration, add a prior on the first pose to set the coordinate frame
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// and a prior on the first landmark to set the scale
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// Also, as iSAM solves incrementally, we must wait until each is observed at least twice before
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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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graph.add(PriorFactor<Pose3>(Symbol('x', 0), poses[0], poseNoise));
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// Add a prior on landmark l0
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noiseModel::Isotropic::shared_ptr pointNoise = noiseModel::Isotropic::Sigma(3, 0.1);
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graph.add(PriorFactor<Point3>(Symbol('l', 0), points[0], pointNoise)); // add directly to graph
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// Add initial guesses to all observed landmarks
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// Intentionally initialize the variables off from the ground truth
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for (size_t j = 0; j < points.size(); ++j)
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initialEstimate.insert(Symbol('l', j), points[j].compose(Point3(-0.25, 0.20, 0.15)));
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} else {
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// Update iSAM with the new factors
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isam.update(graph, initialEstimate);
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Values currentEstimate = isam.estimate();
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cout << "****************************************************" << endl;
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cout << "Frame " << i << ": " << endl;
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currentEstimate.print("Current estimate: ");
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// Clear the factor graph and values for the next iteration
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graph.resize(0);
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initialEstimate.clear();
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}
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}
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}
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return 0;
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return 0;
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}
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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@ -1,89 +0,0 @@
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/* ----------------------------------------------------------------------------
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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 VisualSLAMData.cpp
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* @brief Generate ground-truth simulated data for VisualSLAM examples
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* @author Duy-Nguyen Ta
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*/
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#pragma once
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#include <vector>
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#include <map>
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#include <gtsam/geometry/SimpleCamera.h>
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/* ************************************************************************* */
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/**
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* Simulated data for the visual SLAM examples:
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* - 8 Landmarks: (10,10,10) (-10,10,10) (-10,-10,10) (10,-10,10)
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* (10,10,-10) (-10,10,-10) (-10,-10,-10) (10,-10,-10)
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* - n 90-deg-FoV cameras with the same calibration parameters:
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* f = 50.0, Image: 100x100, center: 50.0, 50.0
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* and ground-truth poses on a circle around the landmarks looking at the world's origin:
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* Rot3(-sin(theta), 0, -cos(theta),
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* cos(theta), 0, -sin(theta),
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* 0, -1, 0 ),
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* Point3(r*cos(theta), r*sin(theta), 0.0)
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* (theta += 2*pi/N)
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* - Measurement noise: 1 pix sigma
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*/
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struct VisualSLAMExampleData {
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gtsam::shared_ptrK sK; // camera calibration parameters
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std::vector<gtsam::Pose3> poses; // ground-truth camera poses
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gtsam::Pose3 odometry; // ground-truth odometry between 2 consecutive poses (simulated data for iSAM)
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std::vector<gtsam::Point3> points; // ground-truth landmarks
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std::map<int, std::vector<gtsam::Point2> > z; // 2D measurements of landmarks in each camera frame
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gtsam::SharedDiagonal noiseZ; // measurement noise (noiseModel::Isotropic::Sigma(2, 5.0f));
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gtsam::SharedDiagonal noiseX; // noise for camera poses
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gtsam::SharedDiagonal noiseL; // noise for landmarks
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static const VisualSLAMExampleData generate() {
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VisualSLAMExampleData data;
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// Landmarks (ground truth)
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data.points.push_back(gtsam::Point3(10.0,10.0,10.0));
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data.points.push_back(gtsam::Point3(-10.0,10.0,10.0));
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data.points.push_back(gtsam::Point3(-10.0,-10.0,10.0));
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data.points.push_back(gtsam::Point3(10.0,-10.0,10.0));
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data.points.push_back(gtsam::Point3(10.0,10.0,-10.0));
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data.points.push_back(gtsam::Point3(-10.0,10.0,-10.0));
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data.points.push_back(gtsam::Point3(-10.0,-10.0,-10.0));
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data.points.push_back(gtsam::Point3(10.0,-10.0,-10.0));
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// Camera calibration parameters
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data.sK = gtsam::shared_ptrK(new gtsam::Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0));
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// n camera poses
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int n = 8;
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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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gtsam::Point3 C = gtsam::Point3(r*cos(theta), r*sin(theta), 0.0);
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gtsam::SimpleCamera camera = gtsam::SimpleCamera::Lookat(C, gtsam::Point3(), gtsam::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, possibly with Gaussian noise
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data.noiseZ = gtsam::noiseModel::Isotropic::Sigma(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])
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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::noiseModel::Diagonal::Sigmas(gtsam::Vector_(6, 0.001, 0.001, 0.001, 0.1, 0.1, 0.1));
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data.noiseL = gtsam::noiseModel::Isotropic::Sigma(3, 0.1);
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return data;
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}
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};
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@ -15,49 +15,123 @@
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* @author Duy-Nguyen Ta
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* @author Duy-Nguyen Ta
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*/
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*/
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#include <gtsam/slam/visualSLAM.h>
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/**
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* A structure-from-motion example with landmarks
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* - The landmarks form a 10 meter cube
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* - The robot rotates around the landmarks, always facing towards the cube
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*/
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// As this is a full 3D problem, we will use Pose3 variables to represent the camera
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// positions and Point3 variables (x, y, z) to represent the landmark coordinates.
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// Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).
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// We will also need a camera object to hold calibration information and perform projections.
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#include <gtsam/geometry/Pose3.h>
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#include <gtsam/geometry/Point3.h>
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#include <gtsam/geometry/Point2.h>
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#include <gtsam/geometry/SimpleCamera.h>
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// Each variable in the system (poses and landmarks) 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/nonlinear/Symbol.h>
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#include <gtsam/nonlinear/Symbol.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
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// In GTSAM, measurement functions are represented as 'factors'. Several common factors
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#include "VisualSLAMData.h"
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// have been provided with the library for solving robotics/SLAM/Bundle Adjustment problems.
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// Here we will use Projection factors to model the camera's landmark observations.
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// Also, we will initialize the robot at some location using a Prior factor.
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/ProjectionFactor.h>
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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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// 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 a
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// trust-region method known as Powell's Degleg
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#include <gtsam/nonlinear/DoglegOptimizer.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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#include <vector>
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using namespace std;
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using namespace std;
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using namespace gtsam;
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using namespace gtsam;
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// Convenience for named keys
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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/* ************************************************************************* */
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/* ************************************************************************* */
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int main(int argc, char* argv[]) {
|
int main(int argc, char* argv[]) {
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|
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||||||
VisualSLAMExampleData data = VisualSLAMExampleData::generate();
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// Define the camera calibration parameters
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Cal3_S2::shared_ptr K(new Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0));
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||||||
|
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/* 1. Create graph *///using the 2D measurements (features) and the calibration data
|
// Define the camera observation noise model
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||||||
visualSLAM::Graph graph;
|
noiseModel::Isotropic::shared_ptr measurementNoise = noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v
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||||||
|
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||||||
/* 2. Add factors to the graph */
|
// Create the set of ground-truth landmarks
|
||||||
// 2a. Measurement factors
|
std::vector<gtsam::Point3> points;
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||||||
for (size_t i=0; i<data.poses.size(); ++i) {
|
points.push_back(gtsam::Point3(10.0,10.0,10.0));
|
||||||
for (size_t j=0; j<data.points.size(); ++j)
|
points.push_back(gtsam::Point3(-10.0,10.0,10.0));
|
||||||
graph.addMeasurement(data.z[i][j], data.noiseZ, X(i), L(j), data.sK);
|
points.push_back(gtsam::Point3(-10.0,-10.0,10.0));
|
||||||
|
points.push_back(gtsam::Point3(10.0,-10.0,10.0));
|
||||||
|
points.push_back(gtsam::Point3(10.0,10.0,-10.0));
|
||||||
|
points.push_back(gtsam::Point3(-10.0,10.0,-10.0));
|
||||||
|
points.push_back(gtsam::Point3(-10.0,-10.0,-10.0));
|
||||||
|
points.push_back(gtsam::Point3(10.0,-10.0,-10.0));
|
||||||
|
|
||||||
|
// Create the set of ground-truth poses
|
||||||
|
std::vector<gtsam::Pose3> poses;
|
||||||
|
double radius = 30.0;
|
||||||
|
int i = 0;
|
||||||
|
double theta = 0.0;
|
||||||
|
gtsam::Point3 up(0,0,1);
|
||||||
|
gtsam::Point3 target(0,0,0);
|
||||||
|
for(; i < 8; ++i, theta += 2*M_PI/8) {
|
||||||
|
gtsam::Point3 position = Point3(radius*cos(theta), radius*sin(theta), 0.0);
|
||||||
|
gtsam::SimpleCamera camera = SimpleCamera::Lookat(position, target, up);
|
||||||
|
poses.push_back(camera.pose());
|
||||||
}
|
}
|
||||||
// 2b. Prior factor for the first pose and point to constraint the system
|
|
||||||
graph.addPosePrior(X(0), data.poses[0], data.noiseX);
|
|
||||||
graph.addPointPrior(L(0), data.points[0], data.noiseL);
|
|
||||||
|
|
||||||
/* 3. Initial estimates for variable nodes, simulated by Gaussian noises */
|
// Create a factor graph
|
||||||
Values initial;
|
NonlinearFactorGraph graph;
|
||||||
for (size_t i=0; i<data.poses.size(); ++i)
|
|
||||||
initial.insert(X(i), data.poses[i]/* *Pose3::Expmap(data.noiseX->sample())*/); // you can add noise if you want
|
|
||||||
for (size_t j=0; j<data.points.size(); ++j)
|
|
||||||
initial.insert(L(j), data.points[j] /*+ Point3(data.noiseL->sample())*/); // you can add noise if you want
|
|
||||||
initial.print("Intial Estimates: ");
|
|
||||||
|
|
||||||
/* 4. Optimize the graph and print results */
|
// Add a prior on pose x1. This indirectly specifies where the origin is.
|
||||||
visualSLAM::Values result = GaussNewtonOptimizer(graph, initial).optimize();
|
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
|
||||||
// visualSLAM::Values result = LevenbergMarquardtOptimizer(graph, initial).optimize();
|
graph.add(PriorFactor<Pose3>(Symbol('x', 0), poses[0], poseNoise)); // add directly to graph
|
||||||
result.print("Final results: ");
|
|
||||||
|
// Simulated measurements from each camera pose, adding them to the factor graph
|
||||||
|
for (size_t i = 0; i < poses.size(); ++i) {
|
||||||
|
for (size_t j = 0; j < points.size(); ++j) {
|
||||||
|
SimpleCamera camera(poses[i], *K);
|
||||||
|
Point2 measurement = camera.project(points[j]);
|
||||||
|
graph.add(GenericProjectionFactor<Pose3, Point3, Cal3_S2>(measurement, measurementNoise, Symbol('x', i), Symbol('l', j), K));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Because the structure-from-motion problem has a scale ambiguity, the problem is still under-constrained
|
||||||
|
// Here we add a prior on the position of the first landmark. This fixes the scale by indicating the distance
|
||||||
|
// between the first camera and the first landmark. All other landmark positions are interpreted using this scale.
|
||||||
|
noiseModel::Isotropic::shared_ptr pointNoise = noiseModel::Isotropic::Sigma(3, 0.1);
|
||||||
|
graph.add(PriorFactor<Point3>(Symbol('l', 0), points[0], pointNoise)); // add directly to graph
|
||||||
|
graph.print("Factor Graph:\n");
|
||||||
|
|
||||||
|
// Create the data structure to hold the initialEstimate estimate to the solution
|
||||||
|
// Intentionally initialize the variables off from the ground truth
|
||||||
|
Values initialEstimate;
|
||||||
|
for (size_t i = 0; i < poses.size(); ++i)
|
||||||
|
initialEstimate.insert(Symbol('x', i), poses[i].compose(Pose3(Rot3::rodriguez(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20))));
|
||||||
|
for (size_t j = 0; j < points.size(); ++j)
|
||||||
|
initialEstimate.insert(Symbol('l', j), points[j].compose(Point3(-0.25, 0.20, 0.15)));
|
||||||
|
initialEstimate.print("Initial Estimates:\n");
|
||||||
|
|
||||||
|
/* Optimize the graph and print results */
|
||||||
|
Values result = DoglegOptimizer(graph, initialEstimate).optimize();
|
||||||
|
result.print("Final results:\n");
|
||||||
|
|
||||||
return 0;
|
return 0;
|
||||||
}
|
}
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue