PCA Analysis for lane detection using laser scan data?
Hi
Im trying to detect the corridor area (walls) using 2D Lidar while robot is moving. I would like to use PCA (Principle Component Analysis ) and ROS . I didnt find any examples in ROS using laser scan data and PCA. I find an ROS example of OpenCV using PCA. So this is the code
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
using namespace std;
using namespace cv;
// Function declarations
void drawAxis(Mat&, Point, Point, Scalar, const float);
double getOrientation(const vector<Point> &, Mat&);
void drawAxis(Mat& img, Point p, Point q, Scalar colour, const float scale = 0.2)
{
double angle = atan2( (double) p.y - q.y, (double) p.x - q.x ); // angle in radians
double hypotenuse = sqrt( (double) (p.y - q.y) * (p.y - q.y) + (p.x - q.x) * (p.x - q.x));
// Here we lengthen the arrow by a factor of scale
q.x = (int) (p.x - scale * hypotenuse * cos(angle));
q.y = (int) (p.y - scale * hypotenuse * sin(angle));
line(img, p, q, colour, 1, LINE_AA);
// create the arrow hooks
p.x = (int) (q.x + 9 * cos(angle + CV_PI / 4));
p.y = (int) (q.y + 9 * sin(angle + CV_PI / 4));
line(img, p, q, colour, 1, LINE_AA);
p.x = (int) (q.x + 9 * cos(angle - CV_PI / 4));
p.y = (int) (q.y + 9 * sin(angle - CV_PI / 4));
line(img, p, q, colour, 1, LINE_AA);
}
double getOrientation(const vector<Point> &pts, Mat &img)
{
//Construct a buffer used by the pca analysis
int sz = static_cast<int>(pts.size());
Mat data_pts = Mat(sz, 2, CV_64F);
for (int i = 0; i < data_pts.rows; i++)
{
data_pts.at<double>(i, 0) = pts[i].x;
data_pts.at<double>(i, 1) = pts[i].y;
}
//Perform PCA analysis
PCA pca_analysis(data_pts, Mat(), PCA::DATA_AS_ROW);
//Store the center of the object
Point cntr = Point(static_cast<int>(pca_analysis.mean.at<double>(0, 0)),
static_cast<int>(pca_analysis.mean.at<double>(0, 1)));
//Store the eigenvalues and eigenvectors
vector<Point2d> eigen_vecs(2);
vector<double> eigen_val(2);
for (int i = 0; i < 2; i++)
{
eigen_vecs[i] = Point2d(pca_analysis.eigenvectors.at<double>(i, 0),
pca_analysis.eigenvectors.at<double>(i, 1));
eigen_val[i] = pca_analysis.eigenvalues.at<double>(i);
}
// Draw the principal components
circle(img, cntr, 3, Scalar(255, 0, 255), 2);
Point p1 = cntr + 0.02 * Point(static_cast<int>(eigen_vecs[0].x * eigen_val[0]), static_cast<int>(eigen_vecs[0].y * eigen_val[0]));
Point p2 = cntr - 0.02 * Point(static_cast<int>(eigen_vecs[1].x * eigen_val[1]), static_cast<int>(eigen_vecs[1].y * eigen_val[1]));
drawAxis(img, cntr, p1, Scalar(0, 255, 0), 1);
drawAxis(img, cntr, p2, Scalar(255, 255, 0), 5);
double angle = atan2(eigen_vecs[0].y, eigen_vecs[0].x); // orientation in radians
return angle;
}
int main(int argc, char** argv)
{
// Load image
CommandLineParser parser(argc, argv, "{@input | ../data/pca_test1.jpg | input image}");
parser.about( "This program demonstrates how to use OpenCV PCA to extract the orientation of an object.\n" );
parser.printMessage();
Mat src = imread(parser.get<String>("@input"));
// Check if image is loaded successfully
if(src.empty())
{
cout << "Problem loading image!!!" << endl;
return EXIT_FAILURE;
}
imshow("src", src);
// Convert image to ...
Did you ever solve?
I'd be happy to make a tutorial but I'm working through it myself first