Vins-Fusion Source: https://github.com/HKUST-Aerial-Robotics/VINS-Fusion
Summary
You should project needs, focusing on learning stereo + gps integration
Reprinted several write better blog
1. Meng new school VINS-Fusion (three) ------ eyes and GPS integration
The main function file
with the program entry in the GPS fused KITTIGPSTest.cpp file, data KITTI dataset
dataset http://www.cvlibs.net/datasets/kitti/raw_data.php
to [2011_10_03_drive_0027_synced] Example
https: / /s3.eu-central-1.amazonaws.com/avg-kitti/raw_data/2011_10_03_drive_0027/2011_10_03_drive_0027_sync.zip
The main function is similar to the previous main function should be carried out ros node and reading the parameters, except that the image data set and the gps data files are read mode, the data is encapsulated into ros gps of data sensor_msgs :: NavSatFix type, the topic is broadcast to gps out, and binocular images directly into the estimator be vio, will re-broadcast the results of vio get out, easy to subscribe and integration with the back of the GPS.
global_fusion
all gps fused in global_fusion folder, the file entry of the file portion is a rosnode globalOptNode.cpp, the main function is very short, as follows
int main(int argc, char **argv)
{
ros::init(argc, argv, "globalEstimator");
ros::NodeHandle n("~");
global_path = &globalEstimator.global_path;
ros::Subscriber sub_GPS = n.subscribe("/gps", 100, GPS_callback);
ros::Subscriber sub_vio = n.subscribe("/vins_estimator/odometry", 100, vio_callback);
pub_global_path = n.advertise<nav_msgs::Path>("global_path", 100);
pub_global_odometry = n.advertise<nav_msgs::Odometry>("global_odometry", 100);
pub_car = n.advertise<visualization_msgs::MarkerArray>("car_model", 1000);
ros::spin();
return 0;
}
Code is very simple, subscription topic (/ gps) receiving and processing data GPS_callback gps callback, the subscription topic (/ vins_estimator / odometry) positioned to receive and process data in vio vio_callback callback function.
And generates three publisher for the results to show on the rviz.
GlobalOptimization类
所有的融合算法都是在GlobalOptimization类中，对应的文件为globalOpt.h和globalOpt.cpp，并且ceres优化器的相关定义在Factors.h中。
GlobalOptimization类中的函数和变量如下
class GlobalOptimization
{
public:
GlobalOptimization();
~GlobalOptimization();
void inputGPS(double t, double latitude, double longitude, double altitude, double posAccuracy);
void inputOdom(double t, Eigen::Vector3d OdomP, Eigen::Quaterniond OdomQ);
void getGlobalOdom(Eigen::Vector3d &odomP, Eigen::Quaterniond &odomQ);
nav_msgs::Path global_path;
private:
void GPS2XYZ(double latitude, double longitude, double altitude, double* xyz);
void optimize();
void updateGlobalPath();
// format t, tx,ty,tz,qw,qx,qy,qz
map<double, vector<double>> localPoseMap;
map<double, vector<double>> globalPoseMap;
map<double, vector<double>> GPSPositionMap;
bool initGPS;
bool newGPS;
GeographicLib::LocalCartesian geoConverter;
std::mutex mPoseMap;
Eigen::Matrix4d WGPS_T_WVIO;
Eigen::Vector3d lastP;
Eigen::Quaterniond lastQ;
std::thread threadOpt;
};
inputGPS和inputOdom两个函数将回调函数中的gps和vio数据导入，getGlobalOdom为获取融合后位姿函数。
GPS2XYZ函数是将GPS的经纬高坐标转换成当前的坐标系的函数，updateGlobalPath顾名思义更新全局位姿函数。
融合算法的实现主要就是在optimize函数中，接下来进行详细介绍。
注意其中几个变量localPoseMap中保存着vio的位姿，GPSPositionMap中保存着gps数据，globalPoseMap中保存着优化后的全局位姿。
融合算法（optimize函数）
void GlobalOptimization::optimize()
{
while(true)
{
if(newGPS)
{
newGPS = false;
printf("global optimization\n");
TicToc globalOptimizationTime;
ceres::Problem problem;
ceres::Solver::Options options;
options.linear_solver_type = ceres::SPARSE_NORMAL_CHOLESKY;
//options.minimizer_progress_to_stdout = true;
//options.max_solver_time_in_seconds = SOLVER_TIME * 3;
options.max_num_iterations = 5;
ceres::Solver::Summary summary;
ceres::LossFunction *loss_function;
loss_function = new ceres::HuberLoss(1.0);
ceres::LocalParameterization* local_parameterization = new ceres::QuaternionParameterization();
//add param
mPoseMap.lock();
int length = localPoseMap.size();
// w^t_i w^q_i
double t_array[length][3];
double q_array[length][4];
map<double, vector<double>>::iterator iter;
iter = globalPoseMap.begin();
for (int i = 0; i < length; i++, iter++)
{
t_array[i][0] = iter->second[0];
t_array[i][1] = iter->second[1];
t_array[i][2] = iter->second[2];
q_array[i][0] = iter->second[3];
q_array[i][1] = iter->second[4];
q_array[i][2] = iter->second[5];
q_array[i][3] = iter->second[6];
problem.AddParameterBlock(q_array[i], 4, local_parameterization);
problem.AddParameterBlock(t_array[i], 3);
}
map<double, vector<double>>::iterator iterVIO, iterVIONext, iterGPS;
int i = 0;
for (iterVIO = localPoseMap.begin(); iterVIO != localPoseMap.end(); iterVIO++, i++)
{
//vio factor
iterVIONext = iterVIO;
iterVIONext++;
if(iterVIONext != localPoseMap.end())
{
Eigen::Matrix4d wTi = Eigen::Matrix4d::Identity();
Eigen::Matrix4d wTj = Eigen::Matrix4d::Identity();
wTi.block<3, 3>(0, 0) = Eigen::Quaterniond(iterVIO->second[3], iterVIO->second[4],
iterVIO->second[5], iterVIO->second[6]).toRotationMatrix();
wTi.block<3, 1>(0, 3) = Eigen::Vector3d(iterVIO->second[0], iterVIO->second[1], iterVIO->second[2]);
wTj.block<3, 3>(0, 0) = Eigen::Quaterniond(iterVIONext->second[3], iterVIONext->second[4],
iterVIONext->second[5], iterVIONext->second[6]).toRotationMatrix();
wTj.block<3, 1>(0, 3) = Eigen::Vector3d(iterVIONext->second[0], iterVIONext->second[1], iterVIONext->second[2]);
Eigen::Matrix4d iTj = wTi.inverse() * wTj;
Eigen::Quaterniond iQj;
iQj = iTj.block<3, 3>(0, 0);
Eigen::Vector3d iPj = iTj.block<3, 1>(0, 3);
ceres::CostFunction* vio_function = RelativeRTError::Create(iPj.x(), iPj.y(), iPj.z(),
iQj.w(), iQj.x(), iQj.y(), iQj.z(),
0.1, 0.01);
problem.AddResidualBlock(vio_function, NULL, q_array[i], t_array[i], q_array[i+1], t_array[i+1]);
}
//gps factor
double t = iterVIO->first;
iterGPS = GPSPositionMap.find(t);
if (iterGPS != GPSPositionMap.end())
{
ceres::CostFunction* gps_function = TError::Create(iterGPS->second[0], iterGPS->second[1],
iterGPS->second[2], iterGPS->second[3]);
//printf("inverse weight %f \n", iterGPS->second[3]);
problem.AddResidualBlock(gps_function, loss_function, t_array[i]);
}
}
//mPoseMap.unlock();
ceres::Solve(options, &problem, &summary);
//std::cout << summary.BriefReport() << "\n";
// update global pose
//mPoseMap.lock();
iter = globalPoseMap.begin();
for (int i = 0; i < length; i++, iter++)
{
vector<double> globalPose{t_array[i][0], t_array[i][1], t_array[i][2],
q_array[i][0], q_array[i][1], q_array[i][2], q_array[i][3]};
iter->second = globalPose;
if(i == length - 1)
{
Eigen::Matrix4d WVIO_T_body = Eigen::Matrix4d::Identity();
Eigen::Matrix4d WGPS_T_body = Eigen::Matrix4d::Identity();
double t = iter->first;
WVIO_T_body.block<3, 3>(0, 0) = Eigen::Quaterniond(localPoseMap[t][3], localPoseMap[t][4],
localPoseMap[t][5], localPoseMap[t][6]).toRotationMatrix();
WVIO_T_body.block<3, 1>(0, 3) = Eigen::Vector3d(localPoseMap[t][0], localPoseMap[t][1], localPoseMap[t][2]);
WGPS_T_body.block<3, 3>(0, 0) = Eigen::Quaterniond(globalPose[3], globalPose[4],
globalPose[5], globalPose[6]).toRotationMatrix();
WGPS_T_body.block<3, 1>(0, 3) = Eigen::Vector3d(globalPose[0], globalPose[1], globalPose[2]);
WGPS_T_WVIO = WGPS_T_body * WVIO_T_body.inverse();
}
}
updateGlobalPath();
//printf("global time %f \n", globalOptimizationTime.toc());
mPoseMap.unlock();
}
std::chrono::milliseconds dura(2000);
std::this_thread::sleep_for(dura);
}
return;
}
首先呢，判断是否有gps数据，整体的算法就是在ceres架构下的优化算法。
所以总体的步骤就是ceres优化的步骤，首先添加优化参数块（AddParameterBlock函数），参数为globalPoseMap中的6维位姿（代码中旋转用四元数表示，所以共7维）。
之后添加CostFunction，通过构建factor重载operator方法实现（具体实现需要学习ceres库）。该部分有两个factor，一个是位姿图优化，另一个则是利用gps进行位置约束。
将factor添加后，进行ceres求解，更新此时gps和vio间的坐标系转换参数，之后再利用updateGlobalPath函数更新位姿。
总而言之，VF的和GPS的融合也是一个优化框架下的松组合，利用GPS的位置约束，使得位姿图优化可以不依赖回环，这是一大优势。