gtsam的使用

例子1：3个位姿点和两个地标点

Pose2(x,y,theta)
Point2(x,y)

• CMakeLists

cmake_minimum_required(VERSION 3.17)
project(gtsam_test)
find_package(Boost COMPONENTS thread filesystem date_time system REQUIRED)
FIND_PACKAGE(GTSAM REQUIRED)
set(CMAKE_CXX_STANDARD 11)
INCLUDE_DIRECTORIES(${Boost_INCLUDE_DIR})
INCLUDE_DIRECTORIES(${GTSAM_INCLUDE_DIR})
INCLUDE_DIRECTORIES("/usr/include/eigen3")

add_executable(gtsam_test main.cpp)
target_link_libraries(gtsam_test ${Boost_LIBRARIES} -lgtsam -ltbb)

• 头文件

#include 
//Pose2:(x,y,theta)
//Point2:(x,y)
#include 
//每个元必须key，在gtsam中
//可以使用整数int，或者symbol自定义key
#include 
//PriorFactor           先验因子(初始化起点)
//BetweenFactor         两个因子之间
//BearingRangeFactor    gtsam中用来确定距离方位（路标点landmark）的因子
#include 
#include 
#include 
//信任区域法
#include 
//协方差
#include 
//获取值
#include 
#include 
#include 
using namespace std;
using namespace gtsam;

• 图构建和优化算法

    // 声明一个优化图和一些变量
    NonlinearFactorGraph graph;
    Symbol x1('x',1),x2('x',2),x3('x',3);
    Symbol l1('l',1),l2('l',2);

	// 起点
    Pose2 prior(0.0,0.0,0.0);
    noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2,0.0,0.0));
    graph.emplace_shared<PriorFactor<Pose2>>(x1,prior,priorNoise);
    
    // 添加两个里程计
    Pose2 odometry1(2.0,0.0,0.0);
    noiseModel::Diagonal::shared_ptr odoNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2,0.2,0.1));
    graph.emplace_shared<BetweenFactor<Pose2>>(x1,x2,odometry1,odoNoise);
    Pose2 odometry2(1.8,0.0,0.0);
    graph.emplace_shared<BetweenFactor<Pose2>>(x2,x3,odometry2,odoNoise);

    // 地图点
    //添加Range-Bearing 到两俩个不同的landmarks
    noiseModel::Diagonal::shared_ptr measurementNoise=noiseModel::Diagonal::Sigmas(Vector2(0.1,0.2));
    Rot2 bearing11=Rot2::fromDegrees(45);//l1,x1,x2,构成的夹角
    Rot2 bearing21=Rot2::fromDegrees(90);//x1,x2,l1,构成的夹角
    Rot2 bearing32=Rot2::fromDegrees(90);//x1,x2,l2,构成的夹角
    double range11=sqrt(8),range21=2.0,range32=2.0;

    //地图点和位姿之间的约束
    graph.emplace_shared<BearingRangeFactor<Pose2, Point2> >(x1, l1, bearing11, range11, measurementNoise);
    graph.emplace_shared<BearingRangeFactor<Pose2, Point2> >(x2, l1, bearing21, range21, measurementNoise);
    graph.emplace_shared<BearingRangeFactor<Pose2, Point2> >(x3, l2, bearing32, range32, measurementNoise);
    
    //初始值
    Values initialEstimate;
    initialEstimate.insert(x1, Pose2(0.5, 0.0, 0.2));
    initialEstimate.insert(x2, Pose2(2.3, 0.1,-0.2));
    initialEstimate.insert(x3, Pose2(4.1, 0.1, 0.1));
    initialEstimate.insert(l1, Point2(1.8, 2.1));
    initialEstimate.insert(l2, Point2(4.1, 1.8));
    
    //求解
    LevenbergMarquardtOptimizer optimizer(graph, initialEstimate);
    Values result = optimizer.optimize();
    //优化后的结果
    result.print("Final Result:\n");
    Marginals marginals(graph, result);
    cout<<"marginals x1:"<< endl << marginals.marginalCovariance(x1)<<endl;
    cout<<"marginals x2:"<< endl << marginals.marginalCovariance(x2)<<endl;
    cout<<"marginals x3:"<< endl << marginals.marginalCovariance(x3)<<endl;
    cout<<"marginals l1:"<< endl << marginals.marginalCovariance(l1)<<endl;
    cout<<"marginals l2:"<< endl << marginals.marginalCovariance(l2)<<endl;

例子2，回环检测

    //声明优化图，有5个坐标点
    NonlinearFactorGraph graph;
    Symbol x1('x', 1), x2('x', 2), x3('x', 3), x4('x', 4), x5('x', 5);
    //初始点x1
    noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
    graph.add(PriorFactor<Pose2>(x1, Pose2(0, 0, 0), priorNoise));

	//所有点的初始值，等于是优化的初值
	Values initial;
    initial.insert(x1, Pose2(0, 0, 0));
    initial.insert(x2, Pose2(2.0, 0, 0));
    initial.insert(x3, Pose2(4.0, 0, pi / 2));
    initial.insert(x4, Pose2(4.0, 2.0, -pi));
    initial.insert(x5, Pose2(2.0, 2.0, -pi / 2));

    //里程计的结果
    noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
    graph.add(BetweenFactor<Pose2>(x1, x2, Pose2(2, 0, 0), model)); //x2-x1
    graph.add(BetweenFactor<Pose2>(x2, x3, Pose2(2, 0, pi/2), model)); // x3-x2
    graph.add(BetweenFactor<Pose2>(x3, x4, Pose2(2, 0, -pi * 3 / 2), model)); //x4-x3
    graph.add(BetweenFactor<Pose2>(x4, x5, Pose2(2, 0, pi / 2), model));//x5-x4
    //回环实际上也就是最一般的一个约束
    graph.add(BetweenFactor<Pose2>(x5, x2, Pose2(2, 0, M_PI_2), model));

    LevenbergMarquardtOptimizer optimer(graph, initial);
    Values result = optimer.optimize();
    result.print("Final result:\n");