用值填充特征矩阵

抱歉打扰。你能知道我该如何修复我的代码吗？它给了我这个错误：

/usr/include/eigen3/Eigen/src/Core/DenseCoeffsBase.h:365: Eigen::DenseCoeffsBase<Derived, 1>::Scalar& Eigen::DenseCoeffsBase<Derived, 1>::operator()(Eigen::Index, Eigen::Index) [with Derived = Eigen::Matrix<float, -1, -1>; Eigen::DenseCoeffsBase<Derived, 1>::Scalar = float; Eigen::Index = long int]: Assertion `row >= 0 && row < rows() && col >= 0 && col < cols()' failed.

我认为我以正确的方式宣布了矩阵。我的错误(我认为(是当我尝试在可能的转换函数中用值填充她时。我认为这可能是错误的转换1(0，位置(=.。我尝试它来注释该行代码并编写一个简单的代码，例如 transform1(0， position(=1;它给出了同样的错误。

很抱歉打扰， 亲切问候。

我的代码 ：

#include "ros/ros.h"
#include <cstdlib>
#include <fstream>
#include <stdio.h>
#include <regex>
#include "sensor_msgs/PointCloud2.h"
#include <pcl_conversions/pcl_conversions.h>
#include <pcl/point_types.h>
#include <pcl/PCLPointCloud2.h>
#include <pcl/conversions.h>
#include <pcl_ros/transforms.h>
#include <boost/shared_ptr.hpp>
#include "pcl_ros/point_cloud.h"
#include "sensor_msgs/PointField.h"
#include <pcl/io/pcd_io.h>
#include "nav_msgs/Odometry.h"
#include "eigen3/Eigen/SVD"
#include "eigen3/Eigen/Dense"
#include "clustering/Track.h"
#include "eigen3/Eigen/Core"
#include "eigen3/Eigen/Sparse"
using namespace Eigen;
int number_od_possible_tracks = 15;
class Odometry_class {
ros::NodeHandle nodeh;
ros::Subscriber sub;
ros::Publisher pub;
public: 
Odometry_class();
void Odometry(const sensor_msgs::PointCloud2 &msg); 
bool Has_pointcloud_min3points(pcl::PointCloud<pcl::PointXYZIVSI> cloud);
bool Possible_transform(pcl::PointCloud<pcl::PointXYZIVSI> cloud);
void Restore_Ids();
pcl::PointCloud<pcl::PointXYZIVSI> last_cloud;
Eigen::MatrixXf transform1 = (Eigen::MatrixXf(3,15));
Eigen::MatrixXf transform2=(Eigen::MatrixXf(3,15));
int consecutive_Ids_current[15];
int consecutive_Ids_previous[15];
int position;
void Transform();
};

Odometry_class::Odometry_class(){
sub = nodeh.subscribe("trackers", 10, &Odometry_class::Odometry, this); 
pub = nodeh.advertise<nav_msgs::Odometry>("odometry", 10); 
}
void Odometry_class::Odometry(const sensor_msgs::PointCloud2 &msg ){
pcl::PCLPointCloud2 pcl_pc2;
pcl_conversions::toPCL(msg, pcl_pc2);
pcl::PointCloud<pcl::PointXYZIVSI> cloud;
pcl::fromPCLPointCloud2(pcl_pc2, cloud); 
if(Possible_transform(cloud)){
ROS_INFO("Transformation is possible");
}
else{
ROS_INFO("Transformation is not possible");
}
Restore_Ids();
pcl::fromPCLPointCloud2(pcl_pc2, last_cloud); 
}


void Odometry_class::Restore_Ids(){
int i=0;
while(consecutive_Ids_current[i]!=0){
consecutive_Ids_current[i]=0;
consecutive_Ids_previous[i]=0;
i++;
}
}
bool Odometry_class::Has_pointcloud_min3points(pcl::PointCloud<pcl::PointXYZIVSI> cloud){
bool has=false;
if(cloud.width>=3){
has=true;
}
return has;
}

bool Odometry_class::Possible_transform(pcl::PointCloud<pcl::PointXYZIVSI> cloud){
bool possible=false;
position=0;
if(Has_pointcloud_min3points(cloud)&Has_pointcloud_min3points(last_cloud)){ 
for(int i=0;i<cloud.width;i++){
for(int j=0;j<last_cloud.width;j++){
if(cloud[i].id==last_cloud[j].id){
consecutive_Ids_current[position]=i;
consecutive_Ids_previous[position]=j;
position++;
}
}
}
}
if(position>=3){
possible=true;
transform1.resize(3,position);
transform2.resize(3,position);
for(int i=0;i<position;i++){
transform1(0,position)= cloud[consecutive_Ids_current[i]].x;
transform1(1,position)= cloud[consecutive_Ids_current[i]].y;
transform1(2,position)= cloud[consecutive_Ids_current[i]].z;
transform2(0,position)= cloud[consecutive_Ids_previous[i]].x;
transform2(1,position)= cloud[consecutive_Ids_previous[i]].y;
transform2(2,position)= cloud[consecutive_Ids_previous[i]].z;
}
}
return possible;
}


int main(int argc, char **argv) {
ros::init(argc, argv, "Odometry");
Odometry_class Odometry; 
ros::spin();
return 0;
}