Obstacle detection for ground robot using aerial image - c++

Question

I want to perform obstacle detection for a ground robot by using a picture taken by a drone of the area the ground robot will cover. Since I have limited background in image processing I am not sure how to carry this out. I tried using the following method but the result is not very accurate. It detects very small edges also and it does not work well with aerial images.
#pragma once
#include <string>
#include <iostream>
#include <vector>
#include "opencv2/opencv.hpp"
using namespace std;
using namespace cv;
//----------------------------------------------------------
// MAIN
//----------------------------------------------------------
int main(int argc, char* argv[])
{
// src image
Mat src;
//grayscale image
Mat gray;
// edges image
Mat edges;
//dst image
Mat dst;
//eroded image
Mat erosion;
//smoothed result
Mat result;
//----------------------------------------------------------
// Image loading
//----------------------------------------------------------
namedWindow("result");
namedWindow("src");
namedWindow("edges");
src = imread("C:/Users/HP/Desktop/SDP/obstacle detection/obstacle detection/obstacle detection/shapes.jpg");
//----------------------------------------------------------
//Specifying size and type of image
//----------------------------------------------------------
edges = Mat::zeros(src.size(), CV_8UC1);
dst = Mat::zeros(src.size(), CV_8UC1);
gray= Mat::zeros(src.size(), CV_8UC1);
erosion = Mat::zeros(src.size(), CV_8UC1);
result = Mat::zeros(src.size(), CV_8UC1);
//----------------------------------------------------------
//Converting from RGB to grayscale
//----------------------------------------------------------
cvtColor(src, gray, COLOR_BGR2GRAY);
//----------------------------------------------------------
//Edge Detetcion using OpenCV Canny Edge Detector function
//----------------------------------------------------------
Canny(gray, edges, 80, 255);
//----------------------------------------------------------
//Filling in the non-obstacle areas with white
//----------------------------------------------------------
for (int i = 0; i<edges.cols; ++i)
{
int j = edges.rows - 1;
for (j = edges.rows - 1; j>0; --j)
{
if (edges.at<uchar>(j, i)>0)
{
break;
}
}
dst(Range(j, dst.rows - 1), Range(i, i + 1)) = 255;
}
//----------------------------------------------------------
// Appying erosion function to remove noise
//----------------------------------------------------------
Mat element = getStructuringElement(MORPH_RECT, Size(10, 10));
erode(dst,erosion,element);
//----------------------------------------------------------
//Smoothing the edges to get result
//----------------------------------------------------------
GaussianBlur(erosion, result, Size(5,5), 4);
//----------------------------------------------------------
// Displaying the intermediate and final resulting images
//----------------------------------------------------------
namedWindow("src", WINDOW_NORMAL);
imshow("src", src);
namedWindow("edges", WINDOW_NORMAL);
imshow("edges", edges);
namedWindow("dst", WINDOW_NORMAL);
imshow("dst", dst);
namedWindow("erosion", WINDOW_NORMAL);
imshow("erosion", erosion);
namedWindow("result", WINDOW_NORMAL);
imshow("result", result);
//----------------------------------------------------------
// Wait key press
//----------------------------------------------------------
waitKey(0);
destroyAllWindows();
return 0;
}
The code takes in an image, converts it to grayscale. Then canny edge detection is used to detect edges of all the objects in the image. This edge detected image s filled with white color starting from the bottom until an edge is detected. The process continues until the whole image is covered. The result is a binary image with white color for areas without obstacles and and black color for obstacles. The opencv function erode is then used to remove unnecessary noise.
I would really appreciate it if I get suggestions on how to improve this or use any other technique.

Answer 1

I suggest thresholding the image for a color range matching the ground. This approach works well, if the color of your ground does not change too much (which is the case in your src image). You might want to check out this OpenCV example (Python).