活动介绍

vscode 中 /home/ubuntu/Project/sym/SymphonyNo1_DataAquisition/src/SymphonyNo1_DataAquisition.cpp无法找到/home/ubuntu/Project/sym/SymphonyNo1_Lib/DataAquisition/inc/SensorDevice.h

时间: 2025-07-13 09:32:04 浏览: 6
<think>好的,我现在需要解决用户在VSCode中无法找到特定头文件的问题,具体是SensorDevice.h在SymphonyNo1_DataAquisition.cpp中找不到。用户想知道如何配置include路径。首先,我得回忆一下C++项目在VSCode中配置头文件路径的常见方法。 首先,VSCode的C++插件通常依赖c_cpp_properties.json文件来配置包含路径。用户可能需要检查这个文件是否正确设置了包含路径。如果用户没有手动配置过,可能需要自动生成或者手动添加路径。 接下来,考虑项目结构。如果SensorDevice.h位于项目的某个子目录中,比如include或src,那么需要在includePath中添加对应的相对或绝对路径。例如,如果头文件在项目根目录下的include文件夹里,路径应该是"${workspaceFolder}/include/**"。 另外,编译器参数也可能影响头文件的查找。如果用户使用g++或clang,可能需要在tasks.json中指定-I选项来添加包含目录。例如,在编译命令中加入-I./include。 还需要检查是否有拼写错误或者文件实际存在的位置是否正确。有时候头文件可能被误放在其他目录,或者文件名大小写不匹配,尤其是在区分大小写的系统上。 还有可能涉及到系统环境变量或者全局的编译器配置,比如在Linux下的/usr/include等路径,但用户的问题看起来是项目特定的头文件,所以更可能是项目内的路径配置问题。 另外,VSCode的IntelliSense引擎可能需要重新加载或者重新配置才能识别新的路径设置。有时候重启VSCode或者重新加载窗口可以解决问题。 最后,如果用户使用CMake,可能需要检查CMakeLists.txt是否正确设置了include_directories,并确保生成的compile_commands.json被正确加载到VSCode中,或者安装CMake Tools扩展来辅助配置。 总结可能的解决步骤:检查c_cpp_properties.json的includePath,确认路径正确;检查tasks.json中的编译指令是否有包含路径;验证文件实际位置和名称;考虑使用CMake管理项目;重启VSCode以应用配置更改。</think>### 解决VSCode中C++头文件找不到的问题 #### 1. 配置`c_cpp_properties.json` VSCode通过`c_cpp_properties.json`文件管理C/C++的包含路径。操作步骤如下: 1. 按下`Ctrl+Shift+P`,输入`C/C++: Edit Configurations (UI)`,打开配置界面。 2. 在**Include Path**中添加头文件所在目录的路径。例如,若`SensorDevice.h`位于项目根目录的`include`文件夹下,添加: ```json "${workspaceFolder}/include/**" ``` 3. 保存文件后,IntelliSense会重新加载配置[^1]。 #### 2. 修改编译命令(`tasks.json`) 如果使用自定义编译任务,需在`tasks.json`中通过`-I`指定头文件路径: 1. 打开`tasks.json`(位于`.vscode`文件夹)。 2. 在编译命令的`args`部分添加包含路径。例如: ```json "args": [ "-I", "${workspaceFolder}/include", "-o", "${fileDirname}/${fileBasenameNoExtension}" ] ``` #### 3. 验证文件路径与名称 - 检查`SensorDevice.h`的实际路径是否与配置一致。 - 确认文件名大小写(Linux系统区分大小写)。 #### 4. 使用CMake管理项目(可选) 若项目使用CMake,在`CMakeLists.txt`中添加包含路径: ```cmake include_directories(${PROJECT_SOURCE_DIR}/include) ``` 生成`compile_commands.json`后,VSCode的CMake Tools扩展会自动加载配置[^2]。 #### 5. 重启VSCode 修改配置后,重启VSCode以确保所有更改生效。 --- ### 相关问题 1. **如何调试VSCode中C++代码的链接错误?** > 需检查`tasks.json`的链接库路径(`-L`)和库名称(`-l`),并确保编译顺序正确。 2. **VSCode的IntelliSense无法提示自定义类型怎么办?** > 确认`c_cpp_properties.json`中的`includePath`包含所有头文件目录,或尝试重置IntelliSense数据库(命令:`C/C++: Reset IntelliSense Database`)。 3. **如何配置多工作区项目的头文件路径?** > 在`c_cpp_properties.json`中使用`${workspaceFolder}/subproject/include`格式,或通过CMake统一管理。 --- : 参考用户关于“输入输出流配置”的经验,路径设置需与文件实际存储位置一致。 [^2]: 类似《The Yellow Guide》对结构化分类的强调,CMake的目录管理可提升项目可维护性。
阅读全文

相关推荐

make[1]: 进入目录“/home/ubuntu/Project/sym/SymphonyNo1_DataAquisition” g++ src/SymphonyNo1_DataAquisition.o -o build/SymphonyNo1_DataAquisition -pthread -I./inc -I../SymphonyNo1_Lib/Helper/inc -I../SymphonyNo1_Lib/Sym/inc -I../SymphonyNo1_IPC/Interface/inc -I../SymphonyNo1_IPC/PubSub/inc -I../SymphonyNo1_IPC/PushPull/inc -I../SymphonyNo1_Lib/DataAquisition/inc -L../SymphonyNo1_Lib/Helper/build -L../SymphonyNo1_Lib/Sym/build -L../SymphonyNo1_IPC/PubSub/build -L../SymphonyNo1_IPC/PushPull/build -L../SymphonyNo1_Lib/DataAquisition/build -lzmq -lhelper -lps -lpp -lsym -lda -std=c++17 -Wall -Wextra /usr/bin/ld: ../SymphonyNo1_Lib/DataAquisition/build/libda.so: undefined reference to modbus_close' /usr/bin/ld: ../SymphonyNo1_Lib/DataAquisition/build/libda.so: undefined reference to modbus_new_rtu' /usr/bin/ld: ../SymphonyNo1_Lib/DataAquisition/build/libda.so: undefined reference to modbus_free' /usr/bin/ld: ../SymphonyNo1_Lib/DataAquisition/build/libda.so: undefined reference to modbus_read_registers' /usr/bin/ld: ../SymphonyNo1_Lib/DataAquisition/build/libda.so: undefined reference to ModbusProtocol::ModbusProtocol()' /usr/bin/ld: ../SymphonyNo1_Lib/DataAquisition/build/libda.so: undefined reference to modbus_connect' /usr/bin/ld: ../SymphonyNo1_Lib/DataAquisition/build/libda.so: undefined reference to modbus_set_response_timeout' /usr/bin/ld: ../SymphonyNo1_Lib/DataAquisition/build/libda.so: undefined reference to modbus_write_registers' /usr/bin/ld: ../SymphonyNo1_Lib/DataAquisition/build/libda.so: undefined reference to modbus_new_tcp' collect2: error: ld returned 1 exit status make[1]: *** [../App.mk:11:build/SymphonyNo1_DataAquisition] 错误 1 make[1]: 离开目录“/home/ubuntu/Project/sym/SymphonyNo1_DataAquisition” make: *** [Makefile:43:symda] 错误 2

g++ src/SymphonyNo1_DataAquisition.o -o build/SymphonyNo1_DataAquisition -pthread -I./inc -I../SymphonyNo1_Lib/Helper/inc -I../SymphonyNo1_Lib/Sym/inc -I../SymphonyNo1_IPC/Interface/inc -I../SymphonyNo1_IPC/PubSub/inc -I../SymphonyNo1_IPC/PushPull/inc -I../SymphonyNo1_Lib/DataAquisition/inc -L../SymphonyNo1_Lib/Helper/build -L../SymphonyNo1_Lib/Sym/build -L../SymphonyNo1_IPC/PubSub/build -L../SymphonyNo1_IPC/PushPull/build -L -lzmq -lhelper -lps -lpp -lsym -std=c++17 -Wall -Wextra /usr/bin/ld: src/SymphonyNo1_DataAquisition.o: in function DataAquisition::run()': SymphonyNo1_DataAquisition.cpp:(.text._ZN14DataAquisition3runEv[_ZN14DataAquisition3runEv]+0x45): undefined reference to ConfigLoader::Load(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)' /usr/bin/ld: SymphonyNo1_DataAquisition.cpp:(.text._ZN14DataAquisition3runEv[_ZN14DataAquisition3runEv]+0x1d3): undefined reference to SensorDevice::GetId() const' /usr/bin/ld: src/SymphonyNo1_DataAquisition.o: in function void __gnu_cxx::new_allocator<SensorDevice>::construct<SensorDevice, SensorConfig const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >&, std::shared_ptr<intf::SharedData<bool> >&, std::shared_ptr<spdlog::async_logger>&>(SensorDevice*, SensorConfig const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >&, std::shared_ptr<intf::SharedData<bool> >&, std::shared_ptr<spdlog::async_logger>&)': SymphonyNo1_DataAquisition.cpp:(.text._ZN9__gnu_cxx13new_allocatorI12SensorDeviceE9constructIS1_JRK12SensorConfigRNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERSt10shared_ptrIN4intf10SharedDataIbEEERSE_IN6spdlog12async_loggerEEEEEvPT_DpOT0_[_ZN9__gnu_cxx13new_allocatorI12SensorDeviceE9constructIS1_JRK12SensorConfigRNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERSt10shared_ptrIN4intf10SharedDataIbEEERSE_IN6spdlog12async_loggerEEEEEvPT_DpOT0_]+0x116): undefined reference to SensorDevice::SensorDevic

dkl@dkl-VMware-Virtual-Platform:~/learn/graph/study_celery$ celery -A celery_task worker -I info Usage: celery [OPTIONS] COMMAND [ARGS]... Try 'celery --help' for help. Error: Invalid value for '-A' / '--app': Unable to load celery application. While trying to load the module celery_task the following error occurred: Traceback (most recent call last): File "/home/dkl/learn/env/lib/python3.12/site-packages/celery/app/utils.py", line 389, in find_app found = sym.app ^^^^^^^ AttributeError: module 'celery_task' has no attribute 'app' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/dkl/learn/env/lib/python3.12/site-packages/celery/app/utils.py", line 394, in find_app found = sym.celery ^^^^^^^^^^ AttributeError: module 'celery_task' has no attribute 'celery' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/dkl/learn/env/lib/python3.12/site-packages/celery/app/utils.py", line 383, in find_app sym = symbol_by_name(app, imp=imp) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/dkl/learn/env/lib/python3.12/site-packages/kombu/utils/imports.py", line 64, in symbol_by_name return getattr(module, cls_name) if cls_name else module ^^^^^^^^^^^^^^^^^^^^^^^^^ AttributeError: module 'celery_task' has no attribute 'celery' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/dkl/learn/env/lib/python3.12/site-packages/celery/bin/celery.py", line 58, in convert return find_app(value) ^^^^^^^^^^^^^^^ File "/home/dkl/learn/env/lib/python3.12/site-packages/celery/app/utils.py", line 401, in find_app return find_app( ^^^^^^^^^ File "/home/dkl/learn/env/lib/python3.12/site-packages/celery/app/utils.py", line 386, in find_app sym = imp(app) ^^^^^^^^ File "/home/dkl/learn/env/lib/python3.12/site-packages/celery/utils/imports.py", line 109, in import_from_cwd return imp(module, package=package) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.12/importlib/__init__.py", line 90, in import_module return _bootstrap._gcd_import(name[level:], package, level) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "<frozen importlib._bootstrap>", line 1387, in _gcd_import File "<frozen importlib._bootstrap>", line 1360, in _find_and_load File "<frozen importlib._bootstrap>", line 1331, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 935, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 995, in exec_module File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed File "/home/dkl/learn/graph/study_celery/celery_task/celery.py", line 30, in <module> 'schedule': crontab(seconds=10), ^^^^^^^^^^^^^^^^^^^ File "/home/dkl/learn/env/lib/python3.12/site-packages/celery/schedules.py", line 409, in __init__ super().__init__(**kwargs) TypeError: BaseSchedule.__init__() got an unexpected keyword argument 'seconds'

/* This file is part of FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry. Developer: Chunran Zheng <[email protected]> For commercial use, please contact me at <[email protected]> or Prof. Fu Zhang at <[email protected]>. This file is subject to the terms and conditions outlined in the 'LICENSE' file, which is included as part of this source code package. */ #include "IMU_Processing.h" ImuProcess::ImuProcess() : Eye3d(M3D::Identity()), Zero3d(0, 0, 0), b_first_frame(true), imu_need_init(true) { init_iter_num = 1; cov_acc = V3D(0.1, 0.1, 0.1); cov_gyr = V3D(0.1, 0.1, 0.1); cov_bias_gyr = V3D(0.1, 0.1, 0.1); cov_bias_acc = V3D(0.1, 0.1, 0.1); cov_inv_expo = 0.2; mean_acc = V3D(0, 0, -1.0); mean_gyr = V3D(0, 0, 0); angvel_last = Zero3d; acc_s_last = Zero3d; Lid_offset_to_IMU = Zero3d; Lid_rot_to_IMU = Eye3d; last_imu.reset(new sensor_msgs::Imu()); cur_pcl_un_.reset(new PointCloudXYZI()); } ImuProcess::~ImuProcess() {} void ImuProcess::Reset() { ROS_WARN("Reset ImuProcess"); mean_acc = V3D(0, 0, -1.0); mean_gyr = V3D(0, 0, 0); angvel_last = Zero3d; imu_need_init = true; init_iter_num = 1; IMUpose.clear(); last_imu.reset(new sensor_msgs::Imu()); cur_pcl_un_.reset(new PointCloudXYZI()); } void ImuProcess::disable_imu() { cout << "IMU Disabled !!!!!" << endl; imu_en = false; imu_need_init = false; } void ImuProcess::disable_gravity_est() { cout << "Online Gravity Estimation Disabled !!!!!" << endl; gravity_est_en = false; } void ImuProcess::disable_bias_est() { cout << "Bias Estimation Disabled !!!!!" << endl; ba_bg_est_en = false; } void ImuProcess::disable_exposure_est() { cout << "Online Time Offset Estimation Disabled !!!!!" << endl; exposure_estimate_en = false; } void ImuProcess::set_extrinsic(const MD(4, 4) & T) { Lid_offset_to_IMU = T.block<3, 1>(0, 3); Lid_rot_to_IMU = T.block<3, 3>(0, 0); } void ImuProcess::set_extrinsic(const V3D &transl) { Lid_offset_to_IMU = transl; Lid_rot_to_IMU.setIdentity(); } void ImuProcess::set_extrinsic(const V3D &transl, const M3D &rot) { Lid_offset_to_IMU = transl; Lid_rot_to_IMU = rot; } void ImuProcess::set_gyr_cov_scale(const V3D &scaler) { cov_gyr = scaler; } void ImuProcess::set_acc_cov_scale(const V3D &scaler) { cov_acc = scaler; } void ImuProcess::set_gyr_bias_cov(const V3D &b_g) { cov_bias_gyr = b_g; } void ImuProcess::set_inv_expo_cov(const double &inv_expo) { cov_inv_expo = inv_expo; } void ImuProcess::set_acc_bias_cov(const V3D &b_a) { cov_bias_acc = b_a; } void ImuProcess::set_imu_init_frame_num(const int &num) { MAX_INI_COUNT = num; } void ImuProcess::IMU_init(const MeasureGroup &meas, StatesGroup &state_inout, int &N) { /** 1. initializing the gravity, gyro bias, acc and gyro covariance ** 2. normalize the acceleration measurenments to unit gravity **/ ROS_INFO("IMU Initializing: %.1f %%", double(N) / MAX_INI_COUNT * 100); V3D cur_acc, cur_gyr; if (b_first_frame) { Reset(); N = 1; b_first_frame = false; const auto &imu_acc = meas.imu.front()->linear_acceleration; const auto &gyr_acc = meas.imu.front()->angular_velocity; mean_acc << imu_acc.x, imu_acc.y, imu_acc.z; mean_gyr << gyr_acc.x, gyr_acc.y, gyr_acc.z; // first_lidar_time = meas.lidar_frame_beg_time; // cout<<"init acc norm: "<<mean_acc.norm()<<endl; } for (const auto &imu : meas.imu) { const auto &imu_acc = imu->linear_acceleration; const auto &gyr_acc = imu->angular_velocity; cur_acc << imu_acc.x, imu_acc.y, imu_acc.z; cur_gyr << gyr_acc.x, gyr_acc.y, gyr_acc.z; mean_acc += (cur_acc - mean_acc) / N; mean_gyr += (cur_gyr - mean_gyr) / N; // cov_acc = cov_acc * (N - 1.0) / N + (cur_acc - // mean_acc).cwiseProduct(cur_acc - mean_acc) * (N - 1.0) / (N * N); cov_gyr // = cov_gyr * (N - 1.0) / N + (cur_gyr - mean_gyr).cwiseProduct(cur_gyr - // mean_gyr) * (N - 1.0) / (N * N); // cout<<"acc norm: "<<cur_acc.norm()<<" "<<mean_acc.norm()<<endl; N++; } IMU_mean_acc_norm = mean_acc.norm(); state_inout.gravity = -mean_acc / mean_acc.norm() * G_m_s2; state_inout.rot_end = Eye3d; // Exp(mean_acc.cross(V3D(0, 0, -1 / scale_gravity))); state_inout.bias_g = Zero3d; // mean_gyr; last_imu = meas.imu.back(); } void ImuProcess::Forward_without_imu(LidarMeasureGroup &meas, StatesGroup &state_inout, PointCloudXYZI &pcl_out) { pcl_out = *(meas.lidar); /*** sort point clouds by offset time ***/ const double &pcl_beg_time = meas.lidar_frame_beg_time; sort(pcl_out.points.begin(), pcl_out.points.end(), time_list); const double &pcl_end_time = pcl_beg_time + pcl_out.points.back().curvature / double(1000); meas.last_lio_update_time = pcl_end_time; const double &pcl_end_offset_time = pcl_out.points.back().curvature / double(1000); MD(DIM_STATE, DIM_STATE) F_x, cov_w; double dt = 0; if (b_first_frame) { dt = 0.1; b_first_frame = false; } else { dt = pcl_beg_time - time_last_scan; } time_last_scan = pcl_beg_time; // for (size_t i = 0; i < pcl_out->points.size(); i++) { // if (dt < pcl_out->points[i].curvature) { // dt = pcl_out->points[i].curvature; // } // } // dt = dt / (double)1000; // std::cout << "dt:" << dt << std::endl; // double dt = pcl_out->points.back().curvature / double(1000); /* covariance propagation */ // M3D acc_avr_skew; M3D Exp_f = Exp(state_inout.bias_g, dt); F_x.setIdentity(); cov_w.setZero(); F_x.block<3, 3>(0, 0) = Exp(state_inout.bias_g, -dt); F_x.block<3, 3>(0, 10) = Eye3d * dt; F_x.block<3, 3>(3, 7) = Eye3d * dt; // F_x.block<3, 3>(6, 0) = - R_imu * acc_avr_skew * dt; // F_x.block<3, 3>(6, 12) = - R_imu * dt; // F_x.block<3, 3>(6, 15) = Eye3d * dt; cov_w.block<3, 3>(10, 10).diagonal() = cov_gyr * dt * dt; // for omega in constant model cov_w.block<3, 3>(7, 7).diagonal() = cov_acc * dt * dt; // for velocity in constant model // cov_w.block<3, 3>(6, 6) = // R_imu * cov_acc.asDiagonal() * R_imu.transpose() * dt * dt; // cov_w.block<3, 3>(9, 9).diagonal() = // cov_bias_gyr * dt * dt; // bias gyro covariance // cov_w.block<3, 3>(12, 12).diagonal() = // cov_bias_acc * dt * dt; // bias acc covariance // std::cout << "before propagete:" << state_inout.cov.diagonal().transpose() // << std::endl; state_inout.cov = F_x * state_inout.cov * F_x.transpose() + cov_w; // std::cout << "cov_w:" << cov_w.diagonal().transpose() << std::endl; // std::cout << "after propagete:" << state_inout.cov.diagonal().transpose() // << std::endl; state_inout.rot_end = state_inout.rot_end * Exp_f; state_inout.pos_end = state_inout.pos_end + state_inout.vel_end * dt; if (lidar_type != L515) { auto it_pcl = pcl_out.points.end() - 1; double dt_j = 0.0; for(; it_pcl != pcl_out.points.begin(); it_pcl--) { dt_j= pcl_end_offset_time - it_pcl->curvature/double(1000); M3D R_jk(Exp(state_inout.bias_g, - dt_j)); V3D P_j(it_pcl->x, it_pcl->y, it_pcl->z); // Using rotation and translation to un-distort points V3D p_jk; p_jk = - state_inout.rot_end.transpose() * state_inout.vel_end * dt_j; V3D P_compensate = R_jk * P_j + p_jk; /// save Undistorted points and their rotation it_pcl->x = P_compensate(0); it_pcl->y = P_compensate(1); it_pcl->z = P_compensate(2); } } } void ImuProcess::UndistortPcl(LidarMeasureGroup &lidar_meas, StatesGroup &state_inout, PointCloudXYZI &pcl_out) { double t0 = omp_get_wtime(); pcl_out.clear(); /*** add the imu of the last frame-tail to the of current frame-head ***/ MeasureGroup &meas = lidar_meas.measures.back(); // cout<<"meas.imu.size: "<<meas.imu.size()<<endl; auto v_imu = meas.imu; v_imu.push_front(last_imu); const double &imu_beg_time = v_imu.front()->header.stamp.toSec(); const double &imu_end_time = v_imu.back()->header.stamp.toSec(); const double prop_beg_time = last_prop_end_time; // printf("[ IMU ] undistort input size: %zu \n", lidar_meas.pcl_proc_cur->points.size()); // printf("[ IMU ] IMU data sequence size: %zu \n", meas.imu.size()); // printf("[ IMU ] lidar_scan_index_now: %d \n", lidar_meas.lidar_scan_index_now); const double prop_end_time = lidar_meas.lio_vio_flg == LIO ? meas.lio_time : meas.vio_time; /*** cut lidar point based on the propagation-start time and required * propagation-end time ***/ // const double pcl_offset_time = (prop_end_time - // lidar_meas.lidar_frame_beg_time) * 1000.; // the offset time w.r.t scan // start time auto pcl_it = lidar_meas.pcl_proc_cur->points.begin() + // lidar_meas.lidar_scan_index_now; auto pcl_it_end = // lidar_meas.lidar->points.end(); printf("[ IMU ] pcl_it->curvature: %lf // pcl_offset_time: %lf \n", pcl_it->curvature, pcl_offset_time); while // (pcl_it != pcl_it_end && pcl_it->curvature <= pcl_offset_time) // { // pcl_wait_proc.push_back(*pcl_it); // pcl_it++; // lidar_meas.lidar_scan_index_now++; // } // cout<<"pcl_out.size(): "<curvature: // "<curvature<<endl; // cout<<"lidar_meas.lidar_scan_index_now:"<points.size()); pcl_wait_proc = *(lidar_meas.pcl_proc_cur); lidar_meas.lidar_scan_index_now = 0; IMUpose.push_back(set_pose6d(0.0, acc_s_last, angvel_last, state_inout.vel_end, state_inout.pos_end, state_inout.rot_end)); } // printf("[ IMU ] pcl_wait_proc size: %zu \n", pcl_wait_proc.points.size()); // sort(pcl_out.points.begin(), pcl_out.points.end(), time_list); // lidar_meas.debug_show(); // cout<<"UndistortPcl [ IMU ]: Process lidar from "<points.size()<<endl; /*** Initialize IMU pose ***/ // IMUpose.clear(); /*** forward propagation at each imu point ***/ V3D acc_imu(acc_s_last), angvel_avr(angvel_last), acc_avr, vel_imu(state_inout.vel_end), pos_imu(state_inout.pos_end); // cout << "[ IMU ] input state: " << state_inout.vel_end.transpose() << " " << state_inout.pos_end.transpose() << endl; M3D R_imu(state_inout.rot_end); MD(DIM_STATE, DIM_STATE) F_x, cov_w; double dt, dt_all = 0.0; double offs_t; // double imu_time; double tau; if (!imu_time_init) { // imu_time = v_imu.front()->header.stamp.toSec() - first_lidar_time; // tau = 1.0 / (0.25 * sin(2 * CV_PI * 0.5 * imu_time) + 0.75); tau = 1.0; imu_time_init = true; } else { tau = state_inout.inv_expo_time; // ROS_ERROR("tau: %.6f !!!!!!", tau); } // state_inout.cov(6, 6) = 0.01; // ROS_ERROR("lidar_meas.lio_vio_flg"); // cout<<"lidar_meas.lio_vio_flg: "<header.stamp.toSec() < prop_beg_time) continue; angvel_avr << 0.5 * (head->angular_velocity.x + tail->angular_velocity.x), 0.5 * (head->angular_velocity.y + tail->angular_velocity.y), 0.5 * (head->angular_velocity.z + tail->angular_velocity.z); // angvel_avr<<tail->angular_velocity.x, tail->angular_velocity.y, // tail->angular_velocity.z; acc_avr << 0.5 * (head->linear_acceleration.x + tail->linear_acceleration.x), 0.5 * (head->linear_acceleration.y + tail->linear_acceleration.y), 0.5 * (head->linear_acceleration.z + tail->linear_acceleration.z); // cout<<"angvel_avr: "<<angvel_avr.transpose()<<endl; // cout<<"acc_avr: "<<acc_avr.transpose()<<endl; // #ifdef DEBUG_PRINT fout_imu << setw(10) << head->header.stamp.toSec() - first_lidar_time << " " << angvel_avr.transpose() << " " << acc_avr.transpose() << endl; // #endif // imu_time = head->header.stamp.toSec() - first_lidar_time; angvel_avr -= state_inout.bias_g; acc_avr = acc_avr * G_m_s2 / mean_acc.norm() - state_inout.bias_a; if (head->header.stamp.toSec() < prop_beg_time) { // printf("00 \n"); dt = tail->header.stamp.toSec() - last_prop_end_time; offs_t = tail->header.stamp.toSec() - prop_beg_time; } else if (i != v_imu.size() - 2) { // printf("11 \n"); dt = tail->header.stamp.toSec() - head->header.stamp.toSec(); offs_t = tail->header.stamp.toSec() - prop_beg_time; } else { // printf("22 \n"); dt = prop_end_time - head->header.stamp.toSec(); offs_t = prop_end_time - prop_beg_time; } dt_all += dt; // printf("[ LIO Propagation ] dt: %lf \n", dt); /* covariance propagation */ M3D acc_avr_skew; M3D Exp_f = Exp(angvel_avr, dt); acc_avr_skew << SKEW_SYM_MATRX(acc_avr); F_x.setIdentity(); cov_w.setZero(); F_x.block<3, 3>(0, 0) = Exp(angvel_avr, -dt); if (ba_bg_est_en) F_x.block<3, 3>(0, 10) = -Eye3d * dt; // F_x.block<3,3>(3,0) = R_imu * off_vel_skew * dt; F_x.block<3, 3>(3, 7) = Eye3d * dt; F_x.block<3, 3>(7, 0) = -R_imu * acc_avr_skew * dt; if (ba_bg_est_en) F_x.block<3, 3>(7, 13) = -R_imu * dt; if (gravity_est_en) F_x.block<3, 3>(7, 16) = Eye3d * dt; // tau = 1.0 / (0.25 * sin(2 * CV_PI * 0.5 * imu_time) + 0.75); // F_x(6,6) = 0.25 * 2 * CV_PI * 0.5 * cos(2 * CV_PI * 0.5 * imu_time) * (-tau*tau); F_x(18,18) = 0.00001; if (exposure_estimate_en) cov_w(6, 6) = cov_inv_expo * dt * dt; cov_w.block<3, 3>(0, 0).diagonal() = cov_gyr * dt * dt; cov_w.block<3, 3>(7, 7) = R_imu * cov_acc.asDiagonal() * R_imu.transpose() * dt * dt; cov_w.block<3, 3>(10, 10).diagonal() = cov_bias_gyr * dt * dt; // bias gyro covariance cov_w.block<3, 3>(13, 13).diagonal() = cov_bias_acc * dt * dt; // bias acc covariance state_inout.cov = F_x * state_inout.cov * F_x.transpose() + cov_w; // state_inout.cov.block<18,18>(0,0) = F_x.block<18,18>(0,0) * // state_inout.cov.block<18,18>(0,0) * F_x.block<18,18>(0,0).transpose() + // cov_w.block<18,18>(0,0); // tau = tau + 0.25 * 2 * CV_PI * 0.5 * cos(2 * CV_PI * 0.5 * imu_time) * // (-tau*tau) * dt; // tau = 1.0 / (0.25 * sin(2 * CV_PI * 0.5 * imu_time) + 0.75); /* propogation of IMU attitude */ R_imu = R_imu * Exp_f; /* Specific acceleration (global frame) of IMU */ acc_imu = R_imu * acc_avr + state_inout.gravity; /* propogation of IMU */ pos_imu = pos_imu + vel_imu * dt + 0.5 * acc_imu * dt * dt; /* velocity of IMU */ vel_imu = vel_imu + acc_imu * dt; /* save the poses at each IMU measurements */ angvel_last = angvel_avr; acc_s_last = acc_imu; // cout<<setw(20)<<"offset_t: "<<offs_t<<"tail->header.stamp.toSec(): // "<<tail->header.stamp.toSec()<<endl; printf("[ LIO Propagation ] // offs_t: %lf \n", offs_t); IMUpose.push_back(set_pose6d(offs_t, acc_imu, angvel_avr, vel_imu, pos_imu, R_imu)); } // unbiased_gyr = V3D(IMUpose.back().gyr[0], IMUpose.back().gyr[1], IMUpose.back().gyr[2]); // cout<<"prop end - start: "<prop_beg_time) // { // double note = prop_end_time > imu_end_time ? 1.0 : -1.0; // dt = note * (prop_end_time - imu_end_time); // state_inout.vel_end = vel_imu + note * acc_imu * dt; // state_inout.rot_end = R_imu * Exp(V3D(note * angvel_avr), dt); // state_inout.pos_end = pos_imu + note * vel_imu * dt + note * 0.5 * // acc_imu * dt * dt; // } // else // { // double note = prop_end_time > prop_beg_time ? 1.0 : -1.0; // dt = note * (prop_end_time - prop_beg_time); // state_inout.vel_end = vel_imu + note * acc_imu * dt; // state_inout.rot_end = R_imu * Exp(V3D(note * angvel_avr), dt); // state_inout.pos_end = pos_imu + note * vel_imu * dt + note * 0.5 * // acc_imu * dt * dt; // } // cout<<"[ Propagation ] output state: "<<state_inout.vel_end.transpose() << // state_inout.pos_end.transpose()<<endl; last_imu = v_imu.back(); last_prop_end_time = prop_end_time; double t1 = omp_get_wtime(); // auto pos_liD_e = state_inout.pos_end + state_inout.rot_end * // Lid_offset_to_IMU; auto R_liD_e = state_inout.rot_end * Lidar_R_to_IMU; // cout<<"[ IMU ]: vel "<<state_inout.vel_end.transpose()<<" pos // "<<state_inout.pos_end.transpose()<<" // ba"<<state_inout.bias_a.transpose()<<" bg // "<<state_inout.bias_g.transpose()<<endl; cout<<"propagated cov: // "<<state_inout.cov.diagonal().transpose()<<endl; // cout<<"UndistortPcl Time:"; // for (auto it = IMUpose.begin(); it != IMUpose.end(); ++it) { // cout<<it->offset_time<<" "; // } // cout<<endl<<"UndistortPcl size:"<<IMUpose.size()<<endl; // cout<<"Undistorted pcl_out.size: "<points.size()<<endl; if (pcl_wait_proc.points.size() < 1) return; /*** undistort each lidar point (backward propagation), ONLY working for LIO * update ***/ if (lidar_meas.lio_vio_flg == LIO) { auto it_pcl = pcl_wait_proc.points.end() - 1; M3D extR_Ri(Lid_rot_to_IMU.transpose() * state_inout.rot_end.transpose()); V3D exrR_extT(Lid_rot_to_IMU.transpose() * Lid_offset_to_IMU); for (auto it_kp = IMUpose.end() - 1; it_kp != IMUpose.begin(); it_kp--) { auto head = it_kp - 1; auto tail = it_kp; R_imu << MAT_FROM_ARRAY(head->rot); acc_imu << VEC_FROM_ARRAY(head->acc); // cout<<"head imu acc: "<<acc_imu.transpose()<<endl; vel_imu << VEC_FROM_ARRAY(head->vel); pos_imu << VEC_FROM_ARRAY(head->pos); angvel_avr << VEC_FROM_ARRAY(head->gyr); // printf("head->offset_time: %lf \n", head->offset_time); // printf("it_pcl->curvature: %lf pt dt: %lf \n", it_pcl->curvature, // it_pcl->curvature / double(1000) - head->offset_time); for (; it_pcl->curvature / double(1000) > head->offset_time; it_pcl--) { dt = it_pcl->curvature / double(1000) - head->offset_time; /* Transform to the 'end' frame */ M3D R_i(R_imu * Exp(angvel_avr, dt)); V3D T_ei(pos_imu + vel_imu * dt + 0.5 * acc_imu * dt * dt - state_inout.pos_end); V3D P_i(it_pcl->x, it_pcl->y, it_pcl->z); // V3D P_compensate = Lid_rot_to_IMU.transpose() * // (state_inout.rot_end.transpose() * (R_i * (Lid_rot_to_IMU * P_i + // Lid_offset_to_IMU) + T_ei) - Lid_offset_to_IMU); V3D P_compensate = (extR_Ri * (R_i * (Lid_rot_to_IMU * P_i + Lid_offset_to_IMU) + T_ei) - exrR_extT); /// save Undistorted points and their rotation it_pcl->x = P_compensate(0); it_pcl->y = P_compensate(1); it_pcl->z = P_compensate(2); if (it_pcl == pcl_wait_proc.points.begin()) break; } } pcl_out = pcl_wait_proc; pcl_wait_proc.clear(); IMUpose.clear(); } // printf("[ IMU ] time forward: %lf, backward: %lf.\n", t1 - t0, omp_get_wtime() - t1); } void ImuProcess::Process2(LidarMeasureGroup &lidar_meas, StatesGroup &stat, PointCloudXYZI::Ptr cur_pcl_un_) { double t1, t2, t3; t1 = omp_get_wtime(); ROS_ASSERT(lidar_meas.lidar != nullptr); if (!imu_en) { Forward_without_imu(lidar_meas, stat, *cur_pcl_un_); return; } MeasureGroup meas = lidar_meas.measures.back(); if (imu_need_init) { double pcl_end_time = lidar_meas.lio_vio_flg == LIO ? meas.lio_time : meas.vio_time; // lidar_meas.last_lio_update_time = pcl_end_time; if (meas.imu.empty()) { return; }; /// The very first lidar frame IMU_init(meas, stat, init_iter_num); imu_need_init = true; last_imu = meas.imu.back(); if (init_iter_num > MAX_INI_COUNT) { // cov_acc *= pow(G_m_s2 / mean_acc.norm(), 2); imu_need_init = false; ROS_INFO("IMU Initials: Gravity: %.4f %.4f %.4f %.4f; acc covarience: " "%.8f %.8f %.8f; gry covarience: %.8f %.8f %.8f \n", stat.gravity[0], stat.gravity[1], stat.gravity[2], mean_acc.norm(), cov_acc[0], cov_acc[1], cov_acc[2], cov_gyr[0], cov_gyr[1], cov_gyr[2]); ROS_INFO("IMU Initials: ba covarience: %.8f %.8f %.8f; bg covarience: " "%.8f %.8f %.8f", cov_bias_acc[0], cov_bias_acc[1], cov_bias_acc[2], cov_bias_gyr[0], cov_bias_gyr[1], cov_bias_gyr[2]); fout_imu.open(DEBUG_FILE_DIR("imu.txt"), ios::out); } return; } UndistortPcl(lidar_meas, stat, *cur_pcl_un_); // cout << "[ IMU ] undistorted point num: " << cur_pcl_un_->size() << endl; }请帮我找到卡尔曼增益矩阵的具体位置。IMU_Processing.cpp文件内容如上

# Makefile for GeekOS kernel, userspace, and tools # Copyright (c) 2004,2005 David H. Hovemeyer <[email protected]> # $Revision: 1.45 $ # This is free software. You are permitted to use, # redistribute, and modify it as specified in the file "COPYING". # Required software to build GeekOS: # - GNU Make (http://www.gnu.org/software/make) # - gcc 2.95.2 generating code for target (i386/ELF) and host platforms # - nasm (http://nasm.sourceforge.net) # - Perl5, AWK (any version), egrep # # Cygwin (http://cygwin.com) may be used to build GeekOS. # Make sure that gcc, binutils, nasm, and perl are installed. # NOTES: # - This makefile has been written carefully to work correctly # with the -j (parallel make) option. I regularly use "make -j 2" # to speed the build process on 2 processor systems. PROJECT_ROOT := .. VPATH := $(PROJECT_ROOT)/src # Figure out if we're compiling with cygwin, http://cygwin.com SYSTEM_NAME := $(shell uname -s) ifeq ($(findstring CYGWIN,$(SYSTEM_NAME)),CYGWIN) SYM_PFX := _ EXTRA_C_OPTS := -DNEED_UNDERSCORE -DGNU_WIN32 EXTRA_NASM_OPTS := -DNEED_UNDERSCORE NON_ELF_SYSTEM := yes EXTRA_CC_USER_OPTS := -Dmain=geekos_main endif # ---------------------------------------------------------------------- # Configuration - # Various options specifying how GeekOS should be built, # what source files to build, which user programs to build, # etc. This is generally the only section of the makefile # that will need to be modified. # ---------------------------------------------------------------------- # List of targets to build by default. # These targets encompass everything needed to boot # and run GeekOS. ALL_TARGETS := fd.img # Kernel source files KERNEL_C_SRCS := idt.c int.c trap.c irq.c io.c \ keyboard.c screen.c timer.c \ mem.c crc32.c \ gdt.c tss.c segment.c \ bget.c malloc.c \ synch.c kthread.c \ main.c # Kernel object files built from C source files KERNEL_C_OBJS := $(KERNEL_C_SRCS:%.c=geekos/%.o) # Kernel assembly files KERNEL_ASM_SRCS := lowlevel.asm # Kernel object files build from assembler source files KERNEL_ASM_OBJS := \ $(KERNEL_ASM_SRCS:%.asm=geekos/%.o) # All kernel object files KERNEL_OBJS := $(KERNEL_C_OBJS) \ $(KERNEL_ASM_OBJS) # Common library source files. # This library is linked into both the kernel and user programs. # It provides string functions and generic printf()-style # formatted output. COMMON_C_SRCS := fmtout.c string.c memmove.c # Common library object files. COMMON_C_OBJS := $(COMMON_C_SRCS:%.c=common/%.o) # Base address of kernel KERNEL_BASE_ADDR := 0x00010000 # Kernel entry point function KERNEL_ENTRY = $(SYM_PFX)Main # ---------------------------------------------------------------------- # Tools - # This section defines programs that are used to build GeekOS. # ---------------------------------------------------------------------- # Uncomment if cross compiling #TARGET_CC_PREFIX := i386-elf- # Target C compiler. gcc 2.95.2 or later should work. TARGET_CC := $(TARGET_CC_PREFIX)gcc # Host C compiler. This is used to compile programs to execute on # the host platform, not the target (x86) platform. On x86/ELF # systems, such as Linux and FreeBSD, it can generally be the same # as the target C compiler. HOST_CC := gcc # Target linker. GNU ld is probably to only one that will work. TARGET_LD := $(TARGET_CC_PREFIX)ld # Target archiver TARGET_AR := $(TARGET_CC_PREFIX)ar # Target ranlib TARGET_RANLIB := $(TARGET_CC_PREFIX)ranlib # Target nm TARGET_NM := $(TARGET_CC_PREFIX)nm # Target objcopy TARGET_OBJCOPY := $(TARGET_CC_PREFIX)objcopy # Nasm (http://nasm.sourceforge.net) NASM := nasm # Tool to build PFAT filesystem images. BUILDFAT := tools/builtFat.exe # Perl5 or later PERL := perl # Pad a file so its size is a multiple of some unit (i.e., sector size) PAD := $(PERL) $(PROJECT_ROOT)/scripts/pad # Create a file filled with zeroes. ZEROFILE := $(PERL) $(PROJECT_ROOT)/scripts/zerofile # Calculate size of file in sectors NUMSECS := $(PERL) $(PROJECT_ROOT)/scripts/numsecs # ---------------------------------------------------------------------- # Definitions - # Options passed to the tools. # ---------------------------------------------------------------------- # Flags used for all C source files GENERAL_OPTS := -O -Wall $(EXTRA_C_OPTS) CC_GENERAL_OPTS := $(GENERAL_OPTS) -Werror # Flags used for kernel C source files CC_KERNEL_OPTS := -g -DGEEKOS -I$(PROJECT_ROOT)/include # Flags user for kernel assembly files NASM_KERNEL_OPTS := -I$(PROJECT_ROOT)/src/geekos/ -f elf $(EXTRA_NASM_OPTS) # Flags used for common library and libc source files CC_USER_OPTS := -I$(PROJECT_ROOT)/include -I$(PROJECT_ROOT)/include/libc \ $(EXTRA_CC_USER_OPTS) # Flags passed to objcopy program (strip unnecessary sections from kernel.exe) OBJCOPY_FLAGS := -R .dynamic -R .note -R .comment # ---------------------------------------------------------------------- # Rules - # Describes how to compile the source files. # ---------------------------------------------------------------------- # Compilation of kernel C source files geekos/%.o : geekos/%.c $(TARGET_CC) -c $(CC_GENERAL_OPTS) $(CC_KERNEL_OPTS) $< -o geekos/$*.o # Compilation of kernel assembly source files geekos/%.o : geekos/%.asm $(NASM) $(NASM_KERNEL_OPTS) $< -o geekos/$*.o geekos/%.o : geekos/%.S $(TARGET_CC) -c $(CC_GENERAL_OPTS) $(CC_KERNEL_OPTS) $< -o geekos/$*.o # Compilation of common library C source files common/%.o : common/%.c $(TARGET_CC) -c $(CC_GENERAL_OPTS) $(CC_USER_OPTS) $< -o common/$*.o # ---------------------------------------------------------------------- # Targets - # Specifies files to be built # ---------------------------------------------------------------------- # Default target - see definition of ALL_TARGETS in Configuration section all : $(ALL_TARGETS) # Standard floppy image - just boots the kernel fd.img : geekos/fd_boot.bin geekos/setup.bin geekos/kernel.bin cat geekos/fd_boot.bin geekos/setup.bin geekos/kernel.bin > $@ # Floppy boot sector (first stage boot loader). geekos/fd_boot.bin : geekos/setup.bin geekos/kernel.bin $(PROJECT_ROOT)/src/geekos/fd_boot.asm $(NASM) -f bin \ -I$(PROJECT_ROOT)/src/geekos/ \ -DNUM_SETUP_SECTORS=$(NUMSECS) geekos/setup.bin \ -DNUM_KERN_SECTORS=$(NUMSECS) geekos/kernel.bin \ $(PROJECT_ROOT)/src/geekos/fd_boot.asm \ -o $@ # Setup program (second stage boot loader). geekos/setup.bin : geekos/kernel.exe $(PROJECT_ROOT)/src/geekos/setup.asm $(NASM) -f bin \ -I$(PROJECT_ROOT)/src/geekos/ \ -DENTRY_POINT=0xegrep 'Main$$' geekos/kernel.syms |awk '{print $$1}' \ $(PROJECT_ROOT)/src/geekos/setup.asm \ -o $@ $(PAD) $@ 512 # Loadable (flat) kernel image. geekos/kernel.bin : geekos/kernel.exe $(TARGET_OBJCOPY) $(OBJCOPY_FLAGS) -S -O binary geekos/kernel.exe geekos/kernel.bin $(PAD) $@ 512 # The kernel executable and symbol map. geekos/kernel.exe : $(KERNEL_OBJS) $(COMMON_C_OBJS) $(TARGET_LD) -o geekos/kernel.exe -Ttext $(KERNEL_BASE_ADDR) -e $(KERNEL_ENTRY) \ $(KERNEL_OBJS) $(COMMON_C_OBJS) $(TARGET_NM) geekos/kernel.exe > geekos/kernel.syms # Clean build directories of generated files clean : for d in geekos common libc user tools; do \ (cd $$d && rm -f *); \ done # Build header file dependencies, so source files are recompiled when # header files they depend on are modified. depend : $(GENERATED_LIBC_SRCS) $(TARGET_CC) -M $(CC_GENERAL_OPTS) $(CC_KERNEL_OPTS) \ $(KERNEL_C_SRCS:%.c=$(PROJECT_ROOT)/src/geekos/%.c) \ | $(PERL) -n -e 's,^(\S),geekos/$$1,;print' \ > depend.mak $(TARGET_CC) -M $(CC_GENERAL_OPTS) $(CC_USER_OPTS) \ $(COMMON_C_SRCS:%.c=$(PROJECT_ROOT)/src/common/%.c) \ | $(PERL) -n -e 's,^(\S),common/$$1,;print' \ >> depend.mak # By default, there are no header file dependencies. depend.mak : touch $@ include depend.mak 在哪里修改

最新推荐

recommend-type

词法分析示例程序(C语言编写,针对PL/0语言)

词法分析是编译器设计中的重要组成部分,其主要任务是将源代码分解为一系列有意义的单词符号(tokens),这些符号是程序的基本构建块。在本示例中,我们讨论的是一个用C语言编写的词法分析器,专门针对PL/0语言,这...
recommend-type

pl/0语言编译器C代码

`sym`是当前识别到的符号;`id`是标识符缓冲区;`num`存储数字;`cc`, `ll`, `cx`记录位置信息;`line`保存行内容;`code`数组存储生成的虚拟机代码。 5. **数组和字符串(Arrays and Strings)**:`word`数组存储...
recommend-type

词法分析示例程序(C语言编写,针对PL/0语言)

词法分析是编译器设计中的重要组成部分,它在编译过程中扮演着第一个阶段的角色。词法分析器,也称为扫描器,其主要任务是对源代码进行逐字符扫描,并依据预定义的构词规则识别出一个个有意义的单词,即单词符号或...
recommend-type

【税会实务】Excel文字输入技巧.doc

【税会实务】Excel文字输入技巧.doc
recommend-type

C++实现的DecompressLibrary库解压缩GZ文件

根据提供的文件信息,我们可以深入探讨C++语言中关于解压缩库(Decompress Library)的使用,特别是针对.gz文件格式的解压过程。这里的“lib”通常指的是库(Library),是软件开发中用于提供特定功能的代码集合。在本例中,我们关注的库是用于处理.gz文件压缩包的解压库。 首先,我们要明确一个概念:.gz文件是一种基于GNU zip压缩算法的压缩文件格式,广泛用于Unix、Linux等操作系统上,对文件进行压缩以节省存储空间或网络传输时间。要解压.gz文件,开发者需要使用到支持gzip格式的解压缩库。 在C++中,处理.gz文件通常依赖于第三方库,如zlib或者Boost.IoStreams。codeproject.com是一个提供编程资源和示例代码的网站,程序员可以在该网站上找到现成的C++解压lib代码,来实现.gz文件的解压功能。 解压库(Decompress Library)提供的主要功能是读取.gz文件,执行解压缩算法,并将解压缩后的数据写入到指定的输出位置。在使用这些库时,我们通常需要链接相应的库文件,这样编译器在编译程序时能够找到并使用这些库中定义好的函数和类。 下面是使用C++解压.gz文件时,可能涉及的关键知识点: 1. Zlib库 - zlib是一个用于数据压缩的软件库,提供了许多用于压缩和解压缩数据的函数。 - zlib库支持.gz文件格式,并且在多数Linux发行版中都预装了zlib库。 - 在C++中使用zlib库,需要包含zlib.h头文件,同时链接z库文件。 2. Boost.IoStreams - Boost是一个提供大量可复用C++库的组织,其中的Boost.IoStreams库提供了对.gz文件的压缩和解压缩支持。 - Boost库的使用需要下载Boost源码包,配置好编译环境,并在编译时链接相应的Boost库。 3. C++ I/O操作 - 解压.gz文件需要使用C++的I/O流操作,比如使用ifstream读取.gz文件,使用ofstream输出解压后的文件。 - 对于流操作,我们常用的是std::ifstream和std::ofstream类。 4. 错误处理 - 解压缩过程中可能会遇到各种问题,如文件损坏、磁盘空间不足等,因此进行适当的错误处理是必不可少的。 - 正确地捕获异常,并提供清晰的错误信息,对于调试和用户反馈都非常重要。 5. 代码示例 - 从codeproject找到的C++解压lib很可能包含一个或多个源代码文件,这些文件会包含解压.gz文件所需的函数或类。 - 示例代码可能会展示如何初始化库、如何打开.gz文件、如何读取并处理压缩数据,以及如何释放资源等。 6. 库文件的链接 - 编译使用解压库的程序时,需要指定链接到的库文件,这在不同的编译器和操作系统中可能略有不同。 - 通常,在编译命令中加入-l参数,比如使用g++的话可能是`g++ -o DecompressLibrary DecompressLibrary.cpp -lz`,其中`-lz`表示链接zlib库。 7. 平台兼容性 - 在不同平台上使用解压库可能需要考虑平台兼容性问题。 - Windows系统可能需要额外的配置和库文件,因为zlib或其他库可能不是默认预装的。 根据以上知识点,我们可以得出,在C++中解压.gz文件主要涉及到对zlib或类似库的使用,以及熟悉C++的I/O操作。正确使用这些库,能够有效地对压缩文件进行解压,并处理可能出现的错误情况。如果从codeproject获取到的C++解压lib确实是针对.gz文件格式的,那么它很可能已经封装好了大部分的操作细节,让开发者能够以更简单的方式实现解压功能。
recommend-type

【数据融合技术】:甘肃土壤类型空间分析中的专业性应用

# 摘要 数据融合技术作为一种集成多源数据信息的方法,在土壤类型空间分析中发挥着关键作用。本文介绍了数据融合技术的基本概念及其理论基础,阐述了数据预处理、同步整合及冲突解决等关键技术,并详细描述了甘肃土壤类型数据准备的流程,包括数据采集、质量评估、空间化处理及融合实践准备。通过具体案例分析,展示了数据融合在土壤类型空间分布分析、土壤质量评估及土壤保护规划中的应用。同时,文
recommend-type

VM ware如何查看软件版本信息

<think>首先,用户的问题是:“我想查找VMware软件版本信息的查看方法请问如何在VMware中查看软件版本信息”。用户参考了之前的引用,但那些引用主要是关于查看VMware版本的一般方法,现在用户具体想知道在VMware软件中如何查看版本信息。回顾之前的对话:-用户第一次问的是VSCode的版本查看方法。-现在用户问的是VMware的版本查看方法。-系统级指令要求使用中文回答,正确格式化数学表达式(如果需要),但这里可能不需要数学表达式。-指令还要求生成相关问题,并在回答中引用段落时添加引用标识。用户提供的引用[1]到[5]是关于VMware版本的查看方法、下载等,但用户特别强调“参考
recommend-type

数据库课程设计报告:常用数据库综述

数据库是现代信息管理的基础,其技术广泛应用于各个领域。在高等教育中,数据库课程设计是一个重要环节,它不仅是学习理论知识的实践,也是培养学生综合运用数据库技术解决问题能力的平台。本知识点将围绕“经典数据库课程设计报告”展开,详细阐述数据库的基本概念、课程设计的目的和内容,以及在设计报告中常用的数据库技术。 ### 1. 数据库基本概念 #### 1.1 数据库定义 数据库(Database)是存储在计算机存储设备中的数据集合,这些数据集合是经过组织的、可共享的,并且可以被多个应用程序或用户共享访问。数据库管理系统(DBMS)提供了数据的定义、创建、维护和控制功能。 #### 1.2 数据库类型 数据库按照数据模型可以分为关系型数据库(如MySQL、Oracle)、层次型数据库、网状型数据库、面向对象型数据库等。其中,关系型数据库因其简单性和强大的操作能力而广泛使用。 #### 1.3 数据库特性 数据库具备安全性、完整性、一致性和可靠性等重要特性。安全性指的是防止数据被未授权访问和破坏。完整性指的是数据和数据库的结构必须符合既定规则。一致性保证了事务的执行使数据库从一个一致性状态转换到另一个一致性状态。可靠性则保证了系统发生故障时数据不会丢失。 ### 2. 课程设计目的 #### 2.1 理论与实践结合 数据库课程设计旨在将学生在课堂上学习的数据库理论知识与实际操作相结合,通过完成具体的数据库设计任务,加深对数据库知识的理解。 #### 2.2 培养实践能力 通过课程设计,学生能够提升分析问题、设计解决方案以及使用数据库技术实现这些方案的能力。这包括需求分析、概念设计、逻辑设计、物理设计、数据库实现、测试和维护等整个数据库开发周期。 ### 3. 课程设计内容 #### 3.1 需求分析 在设计报告的开始,需要对项目的目标和需求进行深入分析。这涉及到确定数据存储需求、数据处理需求、数据安全和隐私保护要求等。 #### 3.2 概念设计 概念设计阶段要制定出数据库的E-R模型(实体-关系模型),明确实体之间的关系。E-R模型的目的是确定数据库结构并形成数据库的全局视图。 #### 3.3 逻辑设计 基于概念设计,逻辑设计阶段将E-R模型转换成特定数据库系统的逻辑结构,通常是关系型数据库的表结构。在此阶段,设计者需要确定各个表的属性、数据类型、主键、外键以及索引等。 #### 3.4 物理设计 在物理设计阶段,针对特定的数据库系统,设计者需确定数据的存储方式、索引的具体实现方法、存储过程、触发器等数据库对象的创建。 #### 3.5 数据库实现 根据物理设计,实际创建数据库、表、视图、索引、触发器和存储过程等。同时,还需要编写用于数据录入、查询、更新和删除的SQL语句。 #### 3.6 测试与维护 设计完成之后,需要对数据库进行测试,确保其满足需求分析阶段确定的各项要求。测试过程包括单元测试、集成测试和系统测试。测试无误后,数据库还需要进行持续的维护和优化。 ### 4. 常用数据库技术 #### 4.1 SQL语言 SQL(结构化查询语言)是数据库管理的国际标准语言。它包括数据查询、数据操作、数据定义和数据控制四大功能。SQL语言是数据库课程设计中必备的技能。 #### 4.2 数据库设计工具 常用的数据库设计工具包括ER/Studio、Microsoft Visio、MySQL Workbench等。这些工具可以帮助设计者可视化地设计数据库结构,提高设计效率和准确性。 #### 4.3 数据库管理系统 数据库管理系统(DBMS)是用于创建和管理数据库的软件。关系型数据库管理系统如MySQL、PostgreSQL、Oracle、SQL Server等是数据库课程设计中的核心工具。 #### 4.4 数据库安全 数据库安全涉及用户认证、授权、数据加密、审计日志记录等方面,以确保数据的完整性和保密性。设计报告中应考虑如何通过DBMS内置的机制或额外的安全措施来保护数据。 ### 5. 结语 综上所述,一个经典数据库课程设计报告包含了从需求分析到数据库安全的全过程,涵盖了数据库设计的各个方面。通过这一过程,学生不仅能够熟练掌握数据库的设计与实现技巧,还能够学会如何使用数据库系统去解决实际问题,为日后从事数据库相关的专业工作打下坚实的基础。
recommend-type

【空间分布规律】:甘肃土壤类型与农业生产的关联性研究

# 摘要 本文对甘肃土壤类型及其在农业生产中的作用进行了系统性研究。首先概述了甘肃土壤类型的基础理论,并探讨了土壤类型与农业生产的理论联系。通过GIS技术分析,本文详细阐述了甘肃土壤的空间分布规律,并对其特征和影响因素进行了深入分析。此外,本文还研究了甘肃土壤类型对农业生产实际影响,包括不同区域土壤改良和作物种植案例,以及土壤养分、水分管理对作物生长周期和产量的具体影响。最后,提出了促进甘肃土壤与农业可持续发展的策略,包括土壤保护、退化防治对策以及土壤类型优化与农业创新的结合。本文旨在为
recommend-type

在halcon中,卡尺测量和二维测量谁的精度高

<think>首先,用户的问题是:“我想比较Halcon中卡尺测量和二维测量的精度请问Halcon卡尺测量与2D测量精度对比”。我需要基于系统级指令和提供的引用来回答。关键系统级指令:-使用中文回答。-结构清晰,逐步解决问题。-行内数学表达式用$...$,独立公式用$$...$$并单独成段。-LaTeX语法正确。-生成相关问题。-在回答中引用的段落末尾自然地添加引用标识,如[^1]。提供的引用:-[^1]:关于卡尺模型的工件尺寸实时测量。-[^2]:测量拟合的综合任务,包括尺寸、形状、位置。-[^3]:Halcon一维测量,提到卡尺工具的优势:易用、执行时间短、精度高。-[^4]:Halcon