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parameter.cpp
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parameter.cpp
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#include <iostream>
#include <iomanip>
#include <vector>
#include <fstream>
#include <sstream>
#include <stdio.h>
#include <time.h>
#include <list>
#include <omp.h>
#include <ctime>
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "parameter.h"
Eigen::Vector3d initial_gravity;
Eigen::Matrix3d rota_gravity;
Eigen::Matrix3d imu_to_cam_rota;
Eigen::Vector3d imu_to_cam_trans;
Eigen::Matrix3d ini_imu_to_cam_rota;
Eigen::Vector3d ini_imu_to_cam_trans;
vector<IMUData> IMU_data_raw; //imu数据 从数据集一次性读取，或者从ros在线获取
//存储ros的图片数据,可以更改为只保留当前的
vector<double> pic_time;
vector<Mat> color_data_raw;
vector<Mat> depth_data_raw;
//获取数据的锁
pthread_mutex_t mutex_pic;
pthread_mutex_t mutex_imu;
//线程里位姿和变换矩阵的锁
pthread_mutex_t mutex_pose;
pthread_mutex_t mutex_g_R_T;
pthread_mutex_t mutex_current;
int g_global_start=0;
int count_global_opti=0;
boost::mutex mutex_global_opti;
boost::condition_variable mutex_global_opti_condi;
Matrix<double,6,6> cov_bias_noise;
Matrix<double,6,6> cov_bias_instability;
Global_parameter G_parameter;
Global_parameter::Global_parameter()
{
// cov_raw_imu
cov_bias_noise.setZero();
cov_bias_instability.setZero();
//10 hours calibration result
//bg ba wihte noise
cov_bias_noise(0,0)=5.1139567119853316e-04;
cov_bias_noise(1,1)=6.0610991075244652e-04 ;
cov_bias_noise(2,2)=5.7900587387976039e-04;
cov_bias_noise(3,3)=1.3233292212738251e-02;
cov_bias_noise(4,4)=1.3343115613847923e-02;
cov_bias_noise(5,5)=1.9740761877965859e-02;
//bg ba instability
cov_bias_instability(0,0)=9.4556072822705115e-06;
cov_bias_instability(1,1)=2.0615735179337626e-05;
cov_bias_instability(2,2)=1.1398053881235606e-05;
cov_bias_instability(3,3)=1.9830106675990003e-04;
cov_bias_instability(4,4)=2.5750643938033958e-04;
cov_bias_instability(5,5)=5.5799332457334456e-04;
// //10 hours calibration average result
// //bg ba wihte noise
// cov_bias_noise(0,0)=5.6550381861024662e-04;
// cov_bias_noise(1,1)=5.6550381861024662e-04 ;
// cov_bias_noise(2,2)=5.6550381861024662e-04;
// cov_bias_noise(3,3)=1.5439056568184012e-02;
// cov_bias_noise(4,4)=1.5439056568184012e-02;
// cov_bias_noise(5,5)=1.5439056568184012e-02;
// //bg ba instability
// cov_bias_instability(0,0)=1.3823132114281247e-05;
// cov_bias_instability(1,1)=1.3823132114281247e-05;
// cov_bias_instability(2,2)=1.3823132114281247e-05;
// cov_bias_instability(3,3)=3.3793361023786139e-04;
// cov_bias_instability(4,4)=3.3793361023786139e-04;
// cov_bias_instability(5,5)=3.3793361023786139e-04;
// //2 hours calibration result
// //bg ba wihte noise
// cov_bias_noise(0,0)=3.8106230059692028e-04;
// cov_bias_noise(1,1)=2.9200298095396083e-04;
// cov_bias_noise(2,2)=2.1578613642294237e-04;
// cov_bias_noise(3,3)=1.3303700969258581e-02;
// cov_bias_noise(4,4)=1.3916430771251031e-02;
// cov_bias_noise(5,5)=2.0199944893677095e-02;
// //bg ba instability
// cov_bias_instability(0,0)=1.5344004341408872e-05;
// cov_bias_instability(1,1)=1.5558996427106962e-05;
// cov_bias_instability(2,2)=2.6572129177411749e-06;
// cov_bias_instability(3,3)=1.9780260848072001e-04;
// cov_bias_instability(4,4)=2.6305990601538999e-04;
// cov_bias_instability(5,5)=6.4837656724543052e-04;
// Matrix<double,6,6> danwei=Matrix<double,6,6>::Identity();
// cov_bias_noise=danwei.norm()*cov_bias_noise.normalized();
// cout<<cov_bias_noise.norm()<<endl;
// cout<<danwei.norm()<<endl;
// cout<<cov_bias_instability.norm()<<endl;
// cout<<cov_bias_noise.normalized()<<endl;
// cout<<cov_bias_noise.norm()<<endl;
// cout<<cov_bias_instability.norm()<<endl;
// cov_bias_noise=1000*cov_bias_noise;
// cov_bias_instability=1000*cov_bias_instability;
// cout<<cov_bias_noise.inverse()<<endl<<endl;
// cout<<cov_bias_instability.inverse()<<endl;
// exit(1);
cv::FileStorage fSettings;
fSettings = cv::FileStorage(CANSHU, cv::FileStorage::READ);
//global variable：
imu_to_cam_rota.setIdentity();
imu_to_cam_trans.setZero();
rota_gravity.setIdentity();
Matrix3d rota;
// rota<<0.02824729928246547, -0.9995993777573096, -0.0017815921098786047,
// -0.028381539190637406, 0.0009795641670923005, -0.9995966830113097,
// 0.9991979675301671, 0.028286470993068413, -0.028342498872561517;
rota<< -0.0270527, -0.999245, -0.0278698,
0.0215538, -0.0284566, 0.999363,
-0.999402, 0.0264348, 0.0223073;
imu_to_cam_rota=rota;
imu_to_cam_trans<<0.04777362000000000108, 0.00223730999999999991, 0.00160071000000000008;
// // Vector3d rota_( 1.44217, -0.801876, 0.769252); //13
// Vector3d rota_( 0.753807, -1.78184 , 1.74913); //14
// Sophus::SO3d rota2 = Sophus::SO3d::exp(rota_);
// imu_to_cam_rota=rota2.matrix();
// imu_to_cam_trans<< -0.0324982, -0.00719391 , -0.0897905; //14
// // 初始偏差不能太大，如果比较大，那么结果重力优化不行，否则可以
// Eigen::Vector3d ro_gravity( 2.5,0.0 , 0.1);
// Sophus::SO3d G_ro=Sophus::SO3d::exp(ro_gravity);
// rota=G_ro.matrix();
ini_imu_to_cam_rota=imu_to_cam_rota;
ini_imu_to_cam_trans=imu_to_cam_trans;
pthread_mutex_init (&mutex_pose,NULL);
pthread_mutex_init (&mutex_g_R_T,NULL);
pthread_mutex_init (&mutex_pic,NULL);
pthread_mutex_init (&mutex_imu,NULL);
pthread_mutex_init (&mutex_imu,NULL);
//jacobian coefficient
xishu_visual = fSettings["xishu_visual"];
xishu_imu_rtv = fSettings["xishu_imu_rtv"];
xishu_imu_bias_change = fSettings["xishu_imu_bias_change"];
xishu_plane = fSettings["xishu_plane"];
//update coefficient
xishu_V = fSettings["xishu_V"];
xishu_R = fSettings["xishu_R"];
xishu_T = fSettings["xishu_T"];
xishu_bg_d = fSettings["xishu_bg_d"];
xishu_ba_d = fSettings["xishu_ba_d"];
xishu_gravity = fSettings["xishu_gravity"];
xishu_rote = fSettings["xishu_rote"];
xishu_trans = fSettings["xishu_trans"];
//路径参数：
string dataset_route1 = fSettings["dataset_route"];
string imu_file_name1 = fSettings["imu_file_name"];
string setting_route1 = fSettings["setting_route"];
dataset_route = dataset_route1;
imu_file_name=imu_file_name1;
setting_route = setting_route1;
TUMDATASET = fSettings["TUMDATASET"];
//运行参数：
sliding_window_length= fSettings["sliding_window_length"];
ini_window_length= fSettings["ini_window_length"];
GN_number = fSettings["GN_number"];
PIC_NUMBER= fSettings["PIC_NUMBER"];
global_opti= fSettings["global_opti"];
gravity_norm = fSettings["gravity_norm"];
drop_wrong_loop_relevant = fSettings["drop_wrong_loop_relevant"];
drop_corres_length = fSettings["drop_corres_length"];
keyframe_track_threshold= fSettings["keyframe_track_threshold"];
vixel = fSettings["vixel"];
time_delay = fSettings["time_delay"];
//运行标志位:
on_line_ros= fSettings["on_line_ros"];
flag_youhua = fSettings["flag_youhua"];
slove_method = fSettings["slove_method"];
imu_locality = fSettings["imu_locality"];
use_cov = fSettings["use_cov"];
drop_wrong_loop = fSettings["drop_wrong_loop"];
visual_loop= fSettings["visual_loop"];
//debug：
exit_thread = fSettings["exit_thread"];
pose_show_thread = fSettings["pose_show_thread"];
out_bias = fSettings["out_bias"];
show_loop = fSettings["show_loop"];
show_cam_imu=fSettings["show_cam_imu"];
show_trajectory=fSettings["show_trajectory"];
show_loop_number = fSettings["show_loop_number"];
out_transformation = fSettings["out_transformation"];
out_residual = fSettings["out_residual"];
save_ply = fSettings["save_ply"];
save_pic_time= fSettings["save_pic_time"];
exit_flag = fSettings["exit_flag"];
//mapping
blur_threshold = fSettings["blur_threshold"];
//show
showCaseMode = fSettings["showCaseMode"];
//camera parameter
camera_width= fSettings["camera_width"];
camera_height= fSettings["camera_height"];
camera_c_fx= fSettings["camera_c_fx"];
camera_c_fy = fSettings["camera_c_fy"];
camera_c_cx = fSettings["camera_c_cx"];
camera_c_cy = fSettings["camera_c_cy"];
camera_depth_scale = fSettings["camera_depth_scale"];
camera_maximum_depth= fSettings["camera_maximum_depth"];
camera_d0 = fSettings["camera_d0"];
camera_d1 = fSettings["camera_d1"];
camera_d2 = fSettings["camera_d2"];
camera_d3 = fSettings["camera_d3"];
camera_d4 = fSettings["camera_d4"];
gravity_opti_method = fSettings["gravity_opti_method"];
//如果视觉优化，那么没有imu定位
if(flag_youhua==2)
{
imu_locality=0;
}
fSettings.release();
}