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 <pcl_conversions/pcl_conversions
.h>
#include <pcl/point_cloud
.h>
#include <pcl/point_types
.h>
#include <Eigen/Dense>
#include <sensor_msgs/CameraInfo
.h>
#include <pcl/filters/voxel_grid
.h>
#include <pcl/features/normal_3d_omp
.h>
#include <pcl/kdtree/kdtree_flann
.h>
#include <pcl/surface/mls
.h>
#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<pcl::PointXYZRGB>& cloud) {
// 1
. 计算点云密度
pcl::search::KdTree<pcl::PointXYZRGB>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZRGB>);
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_radius*2, 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::PointXYZRGB> 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[i*4 + j] = transform_matrix(i, j);
}
h_transform[i*4 + 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[i*3 + 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 + i*3 + 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::PointXYZRGB> 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<pcl::PointXYZRGB> 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<PointXYZRGB> 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;
}
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