后端优化部分的代码主要在Estimator::optimization()这个函数中,我们来看看这个函数作了什么: 首先将vector转换为了数组的形式,因为ceres用的是double数组,所以下面用vector2double做类型转换 /*可以看出来,这里面生成的优化变量由: para_Pose(7维,相机位姿)、 para_SpeedBias(9维,相机速度、加速度偏置、角速度偏置)...
// 基于滑动窗口的紧耦合的非线性优化,残差项的构造和求解 void Estimator::optimization() { TicToc t_whole, t_prepare; vector2double(); //--- 定义问题 定义本地参数化,并添加优化参数--- ceres::Problem problem;// 定义ceres的优化问题 ceres::LossFunction *loss_function;//核函数 //loss_functio...
因为大的捆集作为一个天然的缓冲区,它可以等待并且存储很长一段时间的测量值。工作Gomsf: Graph-optimization based multi-sensor fusion for robust uav pose estimation使用一种基于优化的框架来融合局部视觉惯性里程计(VIO)与GPS测量,这比工作A robust and modular multi-sensor fusion approach applied to mav nav...
VINS-Fusion is an optimization-based multi-sensor state estimator, which achieves accurate self-localization for autonomous applications (drones, cars, and AR/VR). VINS-Fusion is an extension of VINS-Mono, which supports multiple visual-inertial sensor types (mono camera + IMU, stereo cameras + ...
// stereo only initilizationif(STEREO&&!USE_IMU){f_manager.initFramePoseByPnP(frame_count,Ps,Rs,tic,ric);f_manager.triangulate(frame_count,Ps,Rs,tic,ric);optimization();if(frame_count==WINDOW_SIZE){optimization();updateLatestStates();solver_flag=NON_LINEAR;slideWindow();ROS_INFO("Initializ...
VINS-Fusion is an optimization-based multi-sensor state estimator, which achieves accurate self-localization for autonomous applications (drones, cars, and AR/VR). VINS-Fusion is an extension ofVINS-Mono, which supports multiple visual-inertial sensor types (mono camera + IMU, stereo cameras + IM...
void GlobalOptimization::inputGPS(double t, double latitude, double longitude, double altitude, double posAccuracy) { double xyz[3]; GPS2XYZ(latitude, longitude, altitude, xyz); // 将GPS的经纬度转到地面笛卡尔坐标系 vector<double> tmp{xyz[0], xyz[1], xyz[2], posAccuracy}; GPSPositionMap...
GlobalOptimization globalEstimator; ros::Publisher pub_global_odometry, pub_global_path, pub_car; nav_msgs::Path *global_path; doublelast_vio_t = -1; std::queue<sensor_msgs::NavSatFixConstPtr> gpsQueue; std::mutex m_buf; voidpublish_car_model(doublet, Eigen::Vector3d t_w_car, Eigen:...
3. 判断是否有进⾏初始化;若已完成初始化，则调⽤optimization( )，⽤ceres_solver对滑窗进⾏⾮线性优化的求解，优化项主要有四项：边缘化残差、 imu残差、相机重投影残差以及相机与Imu间同步时间差的残差项。否则进⾏相应的初始化过程。4. 本函数中包含⼀个failureDetection()函数,⽤于判断系统在⼀...
VINS-Fusion is an optimization-based multi-sensor state estimator, which achieves accurate self-localization for autonomous applications (drones, cars, and AR/VR). VINS-Fusion is an extension ofVINS-Mono, which supports multiple visual-inertial sensor types (mono camera + IMU, stereo cameras + IM...