GTSAM学习（二）pnp

根据3D点以及相对应的2D像素点，利用GASAM求解最优相机位姿；

#include 
#include 
#include 
#include 
 
using namespace gtsam;
using namespace gtsam::noiseModel;
using symbol_shorthand::X;

/**
 * 创建重投影因子
 * Unary factor on the unknown pose, resulting from meauring the projection of
 * a known 3D point in the image
 */
class ResectioningFactor: public NoiseModelFactor1 {
    typedef NoiseModelFactor1 Base;
 
    Cal3_S2::shared_ptr K_; ///< camera's intrinsic parameters      相机内参
    Point3 P_;              ///< 3D point on the calibration rig    世界坐标系3D点
    Point2 p_;              ///< 2D measurement of the 3D point     投影点观测值(2D)
 
public:
 
    /// Construct factor given known point P and its projection p
    /// 构造函数，给定 (噪声模型，待优化变量，相机内参，2D投影点，世界坐标系3D点
    ResectioningFactor(const SharedNoiseModel& model, const Key& key,
                       const Cal3_S2::shared_ptr& calib, const Point2& p, const Point3& P) :
        Base(model, key), K_(calib), P_(P), p_(p) {
    }
 
    /// evaluate the error
    /// 误差计算，形参(待优化变量，H)
    virtual Vector evaluateError(const Pose3& pose, boost::optional H =
            boost::none) const {
        //Pinhole模型 （相机位姿，内参）
        SimpleCamera camera(pose, *K_);
        // 将世界坐标系3D点，根据相机位姿（待优化变量）,投影到成像平面
        // 与观测值做差，得到重投影误差
        return camera.project(P_, H, boost::none, boost::none) - p_;
    }
};

/*******************************************************************************
 * Camera: f = 1, Image: 100x100, center: 50, 50.0
 * Pose (ground truth): (Xw, -Yw, -Zw, [0,0,2.0]')
 * Known landmarks: 已知
 *    3D Points: (10,10,0) (-10,10,0) (-10,-10,0) (10,-10,0)
 * Perfect measurements:
 *    2D Point:  (55,45)   (45,45)    (45,55)     (55,55)
 *******************************************************************************/
int main(int argc, char* argv[]) {
    /* read camera intrinsic parameters */
    // 相机内参
    Cal3_S2::shared_ptr calib(new Cal3_S2(1, 1, 0, 50, 50));
 
    /* 1. create graph */
    NonlinearFactorGraph graph;
 
    /* 2. add factors to the graph */
    // 噪声模型
    SharedDiagonal measurementNoise = Diagonal::Sigmas(Vector2(0.5, 0.5));
    // 创建上面定义的 重投影因子实例，并加入因子图
    boost::shared_ptr factor;
    // 注意这里的X(1) ===> 实际上使用命名空间 using symbol_shorthand::X;
    graph.emplace_shared(measurementNoise, X(1), calib,
                                             Point2(55, 45), Point3(10, 10, 0));
    graph.emplace_shared(measurementNoise, X(1), calib,
                                             Point2(45, 45), Point3(-10, 10, 0));
    graph.emplace_shared(measurementNoise, X(1), calib,
                                             Point2(45, 55), Point3(-10, -10, 0));
    graph.emplace_shared(measurementNoise, X(1), calib,
                                             Point2(55, 55), Point3(10, -10, 0));
 
    /* 3. Create an initial estimate for the camera pose */
    // 初始化待估计的相机位姿
    Values initial;
    initial.insert(X(1),
                   Pose3(Rot3(1, 0, 0, 0, -1, 0, 0, 0, -1), Point3(0, 0, 1)));
 
    /* 4. Optimize the graph using Levenberg-Marquardt*/
    // LM优化
    Values result = LevenbergMarquardtOptimizer(graph, initial).optimize();
    result.print("Final result:\n");
 
    return 0;
}