C++ and image-based localization

04 Oct 2023

In computer vision, image-based localization refers to the task of determining the position and orientation of a camera relative to a known 3D environment using only images or video. It has a wide range of applications, including augmented reality, robotics, and navigation systems.

In this blog post, we will explore how to implement image-based localization in C++ using OpenCV, a popular computer vision library.

Introduction to Image-based Localization
Implementing Image-based Localization in C++
Conclusion

Introduction to Image-based Localization

Image-based localization involves matching features in a query image with features in a reference image or a 3D model. Once the matches are found, the camera pose can be estimated by solving a perspective-n-point (PnP) problem.

The key steps in image-based localization include:

Feature Extraction: Extracting robust and distinctive features from the images.
Feature Matching: Finding correspondences between the query and reference images.
Pose Estimation: Estimating the camera pose using the matched feature correspondences.

Implementing Image-based Localization in C++

To start with, we need to install OpenCV, which provides extensive support for image processing and computer vision tasks. You can follow the official OpenCV documentation for installation instructions specific to your operating system.

Once OpenCV is set up, we can proceed with implementing image-based localization in C++.

1. Feature Extraction

We can use the ORB (Oriented FAST and rotated BRIEF) feature descriptor implemented in OpenCV to extract features from the images. ORB features are a robust and efficient alternative to more complex feature descriptors like SIFT or SURF.

Here’s an example code snippet on how to extract ORB features in C++ using OpenCV:

#include <opencv2/opencv.hpp>

int main()
{
    cv::Mat img;
    // Load the image
    img = cv::imread("image.jpg");

    cv::Ptr<cv::ORB> orb = cv::ORB::create();
    std::vector<cv::KeyPoint> keypoints;
    cv::Mat descriptors;

    // Detect ORB features
    orb->detectAndCompute(img, cv::noArray(), keypoints, descriptors);

    // Display keypoints
    cv::Mat img_keypoints;
    cv::drawKeypoints(img, keypoints, img_keypoints, cv::Scalar::all(-1), cv::DrawMatchesFlags::DEFAULT);
    cv::imshow("ORB keypoints", img_keypoints);
    cv::waitKey(0);

    return 0;
}

2. Feature Matching

To find correspondences between the query and reference images, we can use the Brute-Force matcher provided by OpenCV. This matcher compares each descriptor in the query image with all descriptors in the reference image to find the best matches.

Here’s an example code snippet on how to perform feature matching using the Brute-Force matcher in C++:

#include <opencv2/opencv.hpp>

int main()
{
    cv::Mat query_img, reference_img;
    // Load query and reference images
    query_img = cv::imread("query_image.jpg");
    reference_img = cv::imread("reference_image.jpg");

    cv::Ptr<cv::ORB> orb = cv::ORB::create();
    std::vector<cv::KeyPoint> query_keypoints, reference_keypoints;
    cv::Mat query_descriptors, reference_descriptors;

    // Detect ORB features in query and reference images
    orb->detectAndCompute(query_img, cv::noArray(), query_keypoints, query_descriptors);
    orb->detectAndCompute(reference_img, cv::noArray(), reference_keypoints, reference_descriptors);

    cv::Ptr<cv::DescriptorMatcher> matcher = cv::DescriptorMatcher::create(cv::DescriptorMatcher::BRUTEFORCE);
    std::vector<cv::DMatch> matches;

    // Perform feature matching
    matcher->match(query_descriptors, reference_descriptors, matches);

    // Display the matched features
    cv::Mat img_matches;
    cv::drawMatches(query_img, query_keypoints, reference_img, reference_keypoints, matches, img_matches);
    cv::imshow("Feature matches", img_matches);
    cv::waitKey(0);

    return 0;
}

3. Pose Estimation

Once we have the feature matches, we can estimate the camera pose using the solvePnP function provided by OpenCV. This function uses a RANSAC-based algorithm to estimate the camera pose from a set of 3D-2D correspondences.

Here’s an example code snippet on how to estimate the camera pose using solvePnP in C++:

#include <opencv2/opencv.hpp>

int main()
{
    std::vector<cv::Point3f> object_points;
    std::vector<cv::Point2f> image_points;
    // Initialize the 3D-2D correspondences

    cv::Mat camera_matrix, distortion_coeffs;
    // Initialize camera matrix and distortion coefficients

    cv::Mat rvec, tvec;
    // Output rotation vector (rvec) and translation vector (tvec) for camera pose

    // Estimate camera pose using solvePnP
    cv::solvePnP(object_points, image_points, camera_matrix, distortion_coeffs, rvec, tvec);

    // Display camera pose
    std::cout << "Rotation Vector (rvec): " << rvec << std::endl;
    std::cout << "Translation Vector (tvec): " << tvec << std::endl;

    return 0;
}

Conclusion

In this blog post, we learned about image-based localization and how to implement it in C++ using OpenCV. We discussed the key steps involved, including feature extraction, feature matching, and pose estimation.

By leveraging the power of OpenCV, we can build robust and accurate image-based localization systems for various applications. Experiment with different feature descriptors and matchers to further enhance the performance of your implementation.

#computerVision #C++

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