pose-estimation

I am trying to estimate the pose and position of a satellite given an image of it. I have a 3D model of the satellite. Using either PnP solvers or POSIT works great when I pick out the point correspondences myself, however I need to to find a method to match the points up automatically. Using a corner detector (best one I found so far is based on the contour) I can find all the relevant points in the image in addition a few spurious points. However I need to match a given point in the image to the correct point in the 3D model. The articles I have read on the subject always seem to assume that

I am using the tflite model for posenet from here . It takes input 1*353*257*3 input image and returns 4 arrays of dimens 1*23*17*17, 1*23*17*34, 1*23*17*64 and 1*23*17*1. The model has an output stride of 16. How can I get the coordinates of all 17 pose points on my input image? I have tried printing the confidence scores from the heatmap of out1 array but I get near to 0.00 values for each pixel. Code is given below: public class MainActivity extends AppCompatActivity { private static final int CAMERA_REQUEST = 1888; private ImageView imageView; private static final int MY_CAMERA_PERMISSION

I have a relative camera pose estimation problem where I am looking at a scene with differently oriented cameras spaced a certain distance apart. Initially, I am computing the essential matrix using the 5 point algorithm and decomposing it to get the R and t of camera 2 w.r.t camera 1. I thought it would be a good idea to do a check by triangulating the two sets of image points into 3D, and then running solvePnP on the 3D-2D correspondences, but the result I get from solvePnP is way off. I am trying to do this to "refine" my pose as the scale can change from one frame to another. Anyway, In

I run simple test for OpenCV camera pose estimation. Having a photo and the same photo scaled up (zoomed in) I use them to detect features, calculate essential matrix and recover camera poses. Mat inliers; Mat E = findEssentialMat(queryPoints, trainPoints, cameraMatrix1, cameraMatrix2, FM_RANSAC, 0.9, MAX_PIXEL_OFFSET, inliers); size_t inliersCount = recoverPose(E, queryGoodPoints, trainGoodPoints, cameraMatrix1, cameraMatrix2, R, T, inliers); So when I specify the original image as the first one, and the zoomed image as the second one, I get translation T close to [0; 0; -1]. However the

I am using OpenCV in a pose estimation algorithm, where I am also attempting to obtain an estimate of the uncertainty of the estimated pose. My pose estimation is performed through 3D-2D correspondences and the PNP algorithm. To achieve uncertainty estimation, I am trying to use the projectPoints() function to reproject 3D points back on the image plane, and use the internally computed Jacobian matrix to obtain the covariance. My feature 'map' contains a planar set of 3D points. My camera trajectory is a simple one: I start close to the features, move backwards and thereby far away from the