用值填充特征矩阵

Question

很抱歉打扰你。你能知道我该如何修复我的代码吗？它给出了这个错误：

/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声明了matrix。我的错误(我认为)是当我试图在可能的转换函数中用值填充她时。我想可能是transform1(0，position)=..我试着注释这几行代码，并编写了一段简单的代码，比如transform1(0，position)=1；它给出了同样的错误。

抱歉打扰了，致以亲切的问候。

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;
}

Answer 1

我想你把循环变量i和上限position弄混了。使用position作为索引将导致访问超出边界的矩阵，而特征库在断言中捕获了这一点。使用循环变量应该可以解决该错误。

transform1(0,i)= cloud[consecutive_Ids_current[i]].x;

对于其余的transform1和transform2赋值也是如此。