The MLS point cloud registration process for highway tunnels based on a coarse-to-fine two-stage segmentation approach is implemented as follows: (1) Adaptive Overlapping Segmentation: An adaptive overlapping segmentation method considering tunnel ancillary facilities distribution is employed, dividing the point cloud into segments with a segment length of [value] through adaptive overlapping partitioning. (2) Feature-Based Initial Registration: Feature points are extracted from segmented point clouds. A weighted ICP registration incorporating spatial consistency constraints through point-tangent plane matching is performed to obtain the transformation matrix. (3) Deviation Verification: The maximum alignment deviation along the driving direction is calculated using ancillary facilities in adaptively segmented point clouds. If the maximum deviation satisfies threshold [threshold], the process proceeds to step (5). Otherwise, it advances to step (4). (4) Refined Cylindrical Segmentation: A uniformly resampled cylindrical fitting method is applied for secondary overlapping segmentation with a refined segment length of [value]. For segments containing ancillary facilities, step (3) is repeated. For facility-free segments, the transformation matrix from adjacent facility-containing segments is adopted for registration before proceeding to step (5). (5) Redundant Point Removal: Using the maximum scanning line interval as neighborhood search radius [radius], each point in the subsequent segment serves as a query point to identify and remove all neighboring points within the [radius] range from the preceding segment. (6) Point Cloud Integration: The deduplicated point clouds from adjacent segments are merged to obtain the complete registered tunnel point cloud. 画个流程图

A fault detection method based on dynamic peakvalley limit under the non-steady conditions

This study aims to develop a methodology for correcting the spatial consistency of highway tunnel multi-temporal vehicle-borne MLS point clouds, to address the limitations of difficulty in determining segmentation regions, low computational efficiency, and local optimum entrapment in automatic registration methods.是否存在语法，时态，逻辑和拼写错误

"This study aims to develop a methodology for correcting the spatial consistency of highway tunnel multi-temporal vehicle-borne MLS point clouds, to address limitations in automatic registration ...

/* 多源异构点云配准数据的滤波及精度分析 */ #include // 点云文件I/O #include // 移动最小二乘曲面重建 #include // 点类型定义 #include // 体素网格下采样 #include // 多线程法线估计 #include // 可视化模块 #include // 半径离群点移除 #include // 统计离群点移除 using namespace std; int main(int argc, char** argv) { pcl::PointCloud::Ptr cloud_a(new pcl::PointCloud); string path = "D:\\新建文件夹\\点云数据集\\Point Cloud Library Files PCD datasets\\kitchen\\Rf3.pcd"; if (pcl::io::loadPCDFile(path, cloud_a) == -1) { PCL_ERROR("Couldn't read file test_pcd.pcd \n"); return (-1); } //多源异构**** pcl::PointCloud::Ptr cloud1(new pcl::PointCloud); pcl::StatisticalOutlierRemoval sor; sor.setInputCloud(cloud_a); sor.setMeanK(15); // 邻域点数量 sor.setStddevMulThresh(1); // 标准差倍数阈值 sor.filter(cloud1); // 输出到cloud1,邻域15个点，距离均值超过1倍标准差视为离群点 pcl::PointCloud::Ptr cloud11(new pcl::PointCloud); pcl::VoxelGrid grid; grid.setInputCloud(cloud1); Eigen::Vector4f leaf_size{ 0.08,0.08,0.08,0 }; grid.setLeafSize(leaf_size); grid.filter(cloud11); pcl::PointCloud::Ptr cloud2(new pcl::PointCloud); pcl::RadiusOutlierRemoval ror; ror.setInputCloud(cloud11); ror.setRadiusSearch(0.1); ror.setMinNeighborsInRadius(1); ror.filter(cloud2); pcl::search::KdTree::Ptr tree(new pcl::search::KdTree); pcl::PointCloud::Ptr mls_points_normal(new pcl::PointCloud); pcl::MovingLeastSquares mls; mls.setInputCloud(cloud2); mls.setComputeNormals(true); // mls.setPolynomialFit(true); mls.setPolynomialOrder(2); // MLS拟合的阶数 mls.setSearchMethod(tree); mls.setSearchRadius(0.3); // 邻域搜索半径 mls.setNumberOfThreads(4); mls.process(mls_points_normal); cout << "mls poits size is: " << mls_points_normal->size() << endl; pcl::io::savePCDFileASCII("多源异构.pcd", mls_points_normal); pcl::PointCloud::Ptr cloud3(new pcl::PointCloud); pcl::io::loadPCDFile("多源异构.pcd", cloud3); // -----------------------------可视化----------------------------------- pcl::visualization::PCLVisualizer viewer("Cloud Viewer"); int v1 = 0; int v2 = 1; viewer.createViewPort(0, 0, 0.5, 1, v1); viewer.createViewPort(0.5, 0, 1, 1, v2); viewer.addPointCloud(cloud_a, "original cloud", v1); viewer.addPointCloud(cloud3, "cloud", v2); viewer.setBackgroundColor(255, 255, 255); viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 1, 0, 0, "original cloud"); viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 4, "original cloud"); viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0, 0, 0, "cloud"); viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 4, "cloud"); viewer.spin(); return 0; }详细解释一下这段代码，如有问题，请给出改进意见。

return -1; } --- ### **关键公式说明** 1. **统计滤波阈值计算** $$ d_{thresh} = \mu + \alpha \cdot \sigma $$ - $\mu$: 邻域距离均值 - $\sigma$: 邻域距离标准差 - $\alpha=1$（代码设定值） 2. **...

详细介绍这个专利的技术方案： #include <ros/ros.h> #include <sensor_msgs/PointCloud2.h> #include <sensor_msgs/point_cloud_conversion.h> #include #include #include #include <Eigen/Dense> #include <sensor_msgs/CameraInfo.h> #include #include #include #include #include <cuda_runtime.h> #include <device_launch_parameters.h> #include <opencv2/opencv.hpp> #include <cv_bridge/cv_bridge.h> using namespace std; using namespace pcl; using namespace Eigen; // 全局变量：存储接收的点云、图像和相机参数 sensor_msgs::PointCloud2 cloud1, cloud2, cloud3; Matrix3d K; sensor_msgs::Image rgb_image; bool rgb_received = false; bool K_initialized = false; bool cloud1_received = false; bool cloud2_received = false; bool cloud3_received = false; // ================ 新增：动态标定参数 ================ Eigen::Matrix4d dynamic_compensation_matrix = Eigen::Matrix4d::Identity(); bool compensation_initialized = false; ros::Time last_calibration_time; const double CALIBRATION_INTERVAL = 300.0; // 每300秒重新标定 // ================ 新增：色度参考板参数 ================ const cv::Vec3f COLOR_REFERENCE(0.5, 0.5, 0.5); // 中性灰参考色 Eigen::Matrix3f color_correction_matrix = Eigen::Matrix3f::Identity(); // ================ 回调函数 ================ void callback1(const sensor_msgs::PointCloud2ConstPtr& msg) { cloud1 = msg; cloud1_received = true; } void callback2(const sensor_msgs::PointCloud2ConstPtr& msg) { cloud2 = msg; cloud2_received = true; } void callback3(const sensor_msgs::PointCloud2ConstPtr& msg) { cloud3 = msg; cloud3_received = true; } void rgbCallback(const sensor_msgs::ImageConstPtr& msg) { rgb_image = msg; rgb_received = true; } void cameraInfoCallback(const sensor_msgs::CameraInfoConstPtr& msg) { K << msg->K[0], msg->K[1], msg->K[2], msg->K[3], msg->K[4], msg->K[5], msg->K[6], msg->K[7], msg->K[8]; K_initialized = true; } // ================ CUDA内核函数 ================ global void transformPointsKernel( const pcl::PointXYZRGB* restrict input_points, pcl::PointXYZRGB* restrict output_points, const float* restrict transform_matrix, const float* restrict compensation_matrix, // 新增：动态补偿矩阵 int num_points) { int idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx < num_points) { float x = input_points[idx].x; float y = input_points[idx].y; float z = input_points[idx].z; // 应用主变换矩阵 float tx = transform_matrix[0]x + transform_matrix[1]y + transform_matrix[2]z + transform_matrix[3]; float ty = transform_matrix[4]x + transform_matrix[5]y + transform_matrix[6]z + transform_matrix[7]; float tz = transform_matrix[8]x + transform_matrix[9]y + transform_matrix[10]z + transform_matrix[11]; // 新增：应用动态补偿矩阵 x = tx; y = ty; z = tz; tx = compensation_matrix[0]x + compensation_matrix[1]y + compensation_matrix[2]z + compensation_matrix[3]; ty = compensation_matrix[4]x + compensation_matrix[5]y + compensation_matrix[6]z + compensation_matrix[7]; tz = compensation_matrix[8]x + compensation_matrix[9]y + compensation_matrix[10]z + compensation_matrix[11]; output_points[idx].x = tx; output_points[idx].y = ty; output_points[idx].z = tz; // 新增：应用色彩校正 float r = input_points[idx].r / 255.0f; float g = input_points[idx].g / 255.0f; float b = input_points[idx].b / 255.0f; // 使用共享内存中的色彩校正矩阵 shared float color_mat[9]; if (threadIdx.x < 9) { color_mat[threadIdx.x] = compensation_matrix[12 + threadIdx.x]; } __syncthreads(); float new_r = color_mat[0]r + color_mat[1]g + color_mat[2]b; float new_g = color_mat[3]r + color_mat[4]g + color_mat[5]b; float new_b = color_mat[6]r + color_mat[7]g + color_mat[8]b; output_points[idx].r = fminf(fmaxf(new_r 255, 0), 255); output_points[idx].g = fminf(fmaxf(new_g * 255, 0), 255); output_points[idx].b = fminf(fmaxf(new_b * 255, 0), 255); } } global void filterKernel( const pcl::PointXYZRGB* input, pcl::PointXYZRGB* output, int* count, float* dynamic_params, // 动态参数数组 [z_min, z_max, x_min, x_max, y_min, y_max, curvature_thresh] int num_points) { int idx = blockIdx.x * blockDim.x + threadIdx.x; if(idx < num_points) { // 动态参数 float z_min = dynamic_params[0]; float z_max = dynamic_params[1]; float x_min = dynamic_params[2]; float x_max = dynamic_params[3]; float y_min = dynamic_params[4]; float y_max = dynamic_params[5]; float curvature_thresh = dynamic_params[6]; // 新增：曲率自适应滤波 bool keep_point = true; // 空间范围过滤 if(input[idx].z < z_min || input[idx].z > z_max || input[idx].x < x_min || input[idx].x > x_max || input[idx].y < y_min || input[idx].y > y_max) { keep_point = false; } // 曲率过滤（简化版，实际应计算局部曲率） shared float local_curvature[256]; local_curvature[threadIdx.x] = 0.0f; if(keep_point) { // 在实际应用中应计算点云曲率 // 这里使用简化方法：z值变化越大，曲率越大 float curvature = 0.0f; for(int i = -2; i <= 2; i++) { if(i == 0) continue; int nidx = idx + i; if(nidx >= 0 && nidx < num_points) { curvature += fabs(input[idx].z - input[nidx].z); } } if(curvature > curvature_thresh) { keep_point = false; } } if(keep_point) { int pos = atomicAdd(count, 1); output[pos] = input[idx]; } } } // ================ 新增：动态标定补偿函数 ================ void updateDynamicCompensation() { // 在实际系统中，这里会检测场景中的标记球并计算补偿矩阵 // 这里使用简化方法：根据时间模拟微小变化 static int call_count = 0; call_count++; // 每10次调用产生微小变化 if(call_count % 10 == 0) { Eigen::Matrix4d delta = Eigen::Matrix4d::Identity(); delta(0, 3) = (rand() % 100 - 50) * 0.0001; // ±0.5mm delta(1, 3) = (rand() % 100 - 50) * 0.0001; delta(2, 3) = (rand() % 100 - 50) * 0.0001; dynamic_compensation_matrix = delta * dynamic_compensation_matrix; compensation_initialized = true; ROS_INFO("Updated dynamic compensation matrix"); } } // ================ 新增：色彩校正函数 ================ void calculateColorCorrection(const cv::Mat& rgb_image) { // 在实际系统中，这里会检测色度参考板并计算校正矩阵 // 这里使用简化方法：随机生成微小变化 static int call_count = 0; call_count++; // 每5次调用产生微小变化 if(call_count % 5 == 0) { Eigen::Matrix3f delta = Eigen::Matrix3f::Identity(); for(int i = 0; i < 3; i++) { for(int j = 0; j < 3; j++) { delta(i, j) += (rand() % 100 - 50) * 0.001f; } } color_correction_matrix = delta * color_correction_matrix; ROS_INFO("Updated color correction matrix"); } } // ================ 新增：非重叠区修复函数 ================ void repairPointCloudHoles(pcl::PointCloud& cloud) { // 1. 计算点云密度 pcl::search::KdTree::Ptr tree(new pcl::search::KdTree); tree->setInputCloud(cloud.makeShared()); // 2. 检测低密度区域（空洞） std::vector<int> low_density_indices; const float density_threshold = 0.1f; // 点/立方米 const float search_radius = 0.1f; // 10cm半径 for(size_t i = 0; i < cloud.size(); i++) { std::vector<int> indices; std::vector<float> distances; if(tree->radiusSearch(cloud[i], search_radius, indices, distances) < density_threshold) { low_density_indices.push_back(i); } } if(low_density_indices.empty()) return; ROS_INFO("Detected %lu low-density points, repairing...", low_density_indices.size()); // 3. 使用径向基函数插值修复（简化版，实际应使用更复杂方法） for(int idx : low_density_indices) { // 在实际系统中，这里会使用RBF插值生成新点 // 这里使用简化方法：取周围点的平均值 std::vector<int> indices; std::vector<float> distances; if(tree->radiusSearch(cloud[idx], search_radius2, indices, distances) > 5) { float x = 0, y = 0, z = 0; int r = 0, g = 0, b = 0; for(int nidx : indices) { x += cloud[nidx].x; y += cloud[nidx].y; z += cloud[nidx].z; r += cloud[nidx].r; g += cloud[nidx].g; b += cloud[nidx].b; } float n = indices.size(); cloud[idx].x = x / n; cloud[idx].y = y / n; cloud[idx].z = z / n; cloud[idx].r = r / n; cloud[idx].g = g / n; cloud[idx].b = b / n; } } } // ================ 点云变换函数 ================ sensor_msgs::PointCloud2 transformPointCloud(const Vector3d& translation, const Vector3d& euler_angles, const sensor_msgs::PointCloud2& cloud) { // 分解欧拉角为绕各轴的旋转 AngleAxisd yawAngle(euler_angles[2], Vector3d::UnitZ()); AngleAxisd pitchAngle(euler_angles[1], Vector3d::UnitY()); AngleAxisd rollAngle(euler_angles[0], Vector3d::UnitX()); Quaterniond quaternion = yawAngle pitchAngle * rollAngle; Matrix3d rotation_matrix = quaternion.toRotationMatrix(); Matrix4d transform_matrix = Matrix4d::Identity(); transform_matrix.block<3, 3>(0, 0) = rotation_matrix; transform_matrix.block<3, 1>(0, 3) = translation; // 转换ROS点云到PCL点云 pcl::PointCloud pcl_cloud; pcl::fromROSMsg(cloud, pcl_cloud); int num_points = pcl_cloud.size(); if (num_points == 0) return cloud; // 准备变换矩阵 float h_transform[12]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 3; ++j) { h_transform[i4 + j] = transform_matrix(i, j); } h_transform[i4 + 3] = transform_matrix(i, 3); } // 新增：准备动态补偿矩阵 float h_compensation[21]; // 12个变换参数 + 9个色彩校正参数 for (int i = 0; i < 4; ++i) { for (int j = 0; j < 3; ++j) { h_compensation[i3 + j] = dynamic_compensation_matrix(j, i); } } // 添加色彩校正矩阵 for (int i = 0; i < 3; ++i) { for (int j = 0; j < 3; ++j) { h_compensation[12 + i3 + j] = color_correction_matrix(i, j); } } // 分配GPU内存 pcl::PointXYZRGB* d_input_points; pcl::PointXYZRGB* d_output_points; float* d_transform; float* d_compensation; cudaMalloc(&d_input_points, num_points * sizeof(pcl::PointXYZRGB)); cudaMalloc(&d_output_points, num_points * sizeof(pcl::PointXYZRGB)); cudaMalloc(&d_transform, 12 * sizeof(float)); cudaMalloc(&d_compensation, 21 * sizeof(float)); // 新增 // 拷贝数据到GPU cudaMemcpy(d_input_points, pcl_cloud.points.data(), num_points * sizeof(pcl::PointXYZRGB), cudaMemcpyHostToDevice); cudaMemcpy(d_transform, h_transform, 12 * sizeof(float), cudaMemcpyHostToDevice); cudaMemcpy(d_compensation, h_compensation, 21 * sizeof(float), cudaMemcpyHostToDevice); // 新增 // 启动CUDA内核 int block_size = 256; int grid_size = (num_points + block_size - 1) / block_size; transformPointsKernel<<<grid_size, block_size>>>(d_input_points, d_output_points, d_transform, d_compensation, // 新增补偿矩阵 num_points); cudaDeviceSynchronize(); // 拷贝结果回CPU cudaMemcpy(pcl_cloud.points.data(), d_output_points, num_points * sizeof(pcl::PointXYZRGB), cudaMemcpyDeviceToHost); // 释放GPU内存 cudaFree(d_input_points); cudaFree(d_output_points); cudaFree(d_transform); cudaFree(d_compensation); // 新增 // 转换回ROS点云格式 sensor_msgs::PointCloud2 transformed_cloud; pcl::toROSMsg(pcl_cloud, transformed_cloud); transformed_cloud.header = cloud.header; return transformed_cloud; } // ================ 点云过滤函数 ================ sensor_msgs::PointCloud2 filterPointCloud(const sensor_msgs::PointCloud2& cloud, double z_min, double z_max, double x_min, double x_max, double y_min, double y_max, float curvature_thresh = 0.05f) { pcl::PointCloud pcl_cloud; pcl::fromROSMsg(cloud, pcl_cloud); const int num_points = pcl_cloud.size(); if(num_points == 0) return cloud; // 准备动态参数 float dynamic_params[7] = { static_cast<float>(z_min), static_cast<float>(z_max), static_cast<float>(x_min), static_cast<float>(x_max), static_cast<float>(y_min), static_cast<float>(y_max), curvature_thresh }; // 分配GPU内存 pcl::PointXYZRGB d_input, d_output; int d_count; float d_params; cudaMalloc(&d_input, num_points * sizeof(pcl::PointXYZRGB)); cudaMalloc(&d_output, num_points * sizeof(pcl::PointXYZRGB)); cudaMalloc(&d_count, sizeof(int)); cudaMalloc(&d_params, 7 * sizeof(float)); // 新增 cudaMemset(d_count, 0, sizeof(int)); cudaMemcpy(d_params, dynamic_params, 7 * sizeof(float), cudaMemcpyHostToDevice); // 新增 // 数据拷贝到GPU cudaMemcpy(d_input, pcl_cloud.points.data(), num_points * sizeof(pcl::PointXYZRGB), cudaMemcpyHostToDevice); // 启动内核 int block_size = 256; int grid_size = (num_points + block_size - 1) / block_size; filterKernel<<<grid_size, block_size>>>( d_input, d_output, d_count, d_params, // 使用动态参数数组 num_points ); cudaDeviceSynchronize(); // 获取过滤后的点数 int h_count; cudaMemcpy(&h_count, d_count, sizeof(int), cudaMemcpyDeviceToHost); pcl::PointCloud filtered_cloud; filtered_cloud.resize(h_count); cudaMemcpy(filtered_cloud.points.data(), d_output, h_count * sizeof(pcl::PointXYZRGB), cudaMemcpyDeviceToHost); // 释放GPU内存 cudaFree(d_input); cudaFree(d_output); cudaFree(d_count); cudaFree(d_params); // 新增 // 转换回ROS消息格式 sensor_msgs::PointCloud2 filtered_msg; pcl::toROSMsg(filtered_cloud, filtered_msg); filtered_msg.header = cloud.header; return filtered_msg; } // ================ 点云合并函数 ================ sensor_msgs::PointCloud2 mergePointClouds(const sensor_msgs::PointCloud2& cloud3_filtered, const sensor_msgs::PointCloud2& cloud1_transformed, const sensor_msgs::PointCloud2& cloud2_transformed) { PointCloud pcl_cloud3, pcl_cloud1, pcl_cloud2, merged_cloud; fromROSMsg(cloud3_filtered, pcl_cloud3); fromROSMsg(cloud1_transformed, pcl_cloud1); fromROSMsg(cloud2_transformed, pcl_cloud2); pcl_cloud3 += pcl_cloud1; pcl_cloud3 += pcl_cloud2; // 新增：修复非重叠区域 repairPointCloudHoles(pcl_cloud3); sensor_msgs::PointCloud2 merged_msg; pcl::toROSMsg(pcl_cloud3, merged_msg); merged_msg.header = cloud3_filtered.header; return merged_msg; } // ================ 主函数 ================ int main(int argc, char** argv) { ros::init(argc, argv, "point_cloud_merger"); ros::NodeHandle nh; ros::Subscriber info_sub = nh.subscribe("/camera/rgb/camera_info", 1, cameraInfoCallback); ros::Subscriber rgb_sub = nh.subscribe("/camera/rgb/image_rect_color", 1, rgbCallback); ros::Subscriber sub1 = nh.subscribe("/camera/depth_registered/points", 1, callback1); ros::Subscriber sub2 = nh.subscribe("/camera_2/depth/points", 1, callback2); ros::Subscriber sub3 = nh.subscribe("/camera_3/depth/points", 1, callback3); // 发布合并后的点云话题 ros::Publisher pub = nh.advertise<sensor_msgs::PointCloud2>("/merged_point_cloud", 5); ros::Rate rate(10); // 10Hz循环处理 // 初始化动态补偿 dynamic_compensation_matrix = Eigen::Matrix4d::Identity(); compensation_initialized = true; last_calibration_time = ros::Time::now(); while (ros::ok()) { // 新增：定期更新动态补偿 if ((ros::Time::now() - last_calibration_time).toSec() > CALIBRATION_INTERVAL) { updateDynamicCompensation(); last_calibration_time = ros::Time::now(); } // 新增：更新色彩校正 if (rgb_received) { cv_bridge::CvImagePtr cv_ptr = cv_bridge::toCvCopy(rgb_image, sensor_msgs::image_encodings::BGR8); calculateColorCorrection(cv_ptr->image); rgb_received = false; } if (K_initialized && cloud1_received && cloud2_received && cloud3_received && compensation_initialized) { // 对点云进行过滤（使用动态参数） sensor_msgs::PointCloud2 cloud1_filtered = filterPointCloud(cloud1, 0.0, 2.5, -3, 3, -5, 5, 0.03f); sensor_msgs::PointCloud2 cloud2_filtered = filterPointCloud(cloud2, 1.87, 2.6, -3, 1.4, -0.65, 0.65, 0.04f); sensor_msgs::PointCloud2 cloud3_filtered = filterPointCloud(cloud3, 1.87, 2.6, -1.4, 1.5, -0.65, 0.65, 0.04f); // 对点云进行坐标变换（包含动态补偿和色彩校正） sensor_msgs::PointCloud2 cloud1_transformed = transformPointCloud( Eigen::Vector3d(-2.94897, 0.05, 0.906158), Eigen::Vector3d(0.0017, -0.0249, -1.5884), cloud1_filtered ); sensor_msgs::PointCloud2 cloud2_transformed = transformPointCloud( Eigen::Vector3d(-2.1, 0, 0.0), Eigen::Vector3d(0, 0, 0), cloud2_filtered ); // 合并点云（包含空洞修复） sensor_msgs::PointCloud2 merged_msg = mergePointClouds( cloud3_filtered, cloud1_transformed, cloud2_transformed ); // 发布合并结果 merged_msg.header.stamp = ros::Time::now(); pub.publish(merged_msg); // 重置标志位 cloud1_received = false; cloud2_received = false; cloud3_received = false; } ros::spinOnce(); rate.sleep(); } return 0; }

- 发布合并后的点云话题（/merged_point_cloud）。 2. **核心创新点**： - **动态标定补偿**：通过updateDynamicCompensation()函数定期更新变换矩阵，补偿机械振动或热变形导致的传感器位姿变化。补偿矩阵每...

Although the objective function is nonlinear, but the multi-temporal MLS point clouds have a small relative rotation error, so that the nonlinear function can be linearized via Taylor series expansion around approximate values. This allows the subsequent solution using linear least-squares methods.英文句子润色

原句是：“Although the objective function is nonlinear, but the multi-temporal MLS point clouds have a small relative rotation error, so that the nonlinear function can be linearized via Taylor series...

ExecuteThread: '1'for queue:'weblogic.kernel.Default(self-tuning)'> <<Mls Kernel>> <> > <1686294015291> <BEA-101020> <[ServletContext@1221363649 app:nkzbank module:nkzBank paih:/nkZBank spec-version:2.5]] Servletfailed with Exception

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刷题模式(附答案)：1+x 证书 Web 前端开发初级实操考试样题（2019）

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