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Multiple-View Geometry
Published in Jian Chen, Bingxi Jia, Kaixiang Zhang, Multi-View Geometry Based Visual Perception and Control of Robotic Systems, 2018
Jian Chen, Bingxi Jia, Kaixiang Zhang
To obtain the pose information from the epipolar geometry, the essential matrix E is decomposed as follows: Perform the SVD decomposition of the essential matrix E as E = Udiag([1,1,0]), there are two possible factorizations E = SR as follows:
Multisensor Map Matching for Pedestrian and Wheelchair Navigation
Published in Hassan A. Karimi, Advanced Location-Based Technologies and Services, 2016
In the next step, given that the camera was calibrated beforehand, we already know the calibration matrix K. Therefore, we can obtain the essential matrix by applying E = K’*F*K. In the last step, the frame-to-frame rotation matrix Ri−1,i
Epipolar geometry
Published in Aditi Majumder, M. Gopi, Introduction to Visual Computing, 2018
Essential matrix E is defined as the fundamental matrix of normalized cameras. A normalized camera is achieved when the coordinates of the camera are normalized and hence the name. Therefore, the intrinsic matrix K of a normalized camera is identity, i.e. K = I. Therefore, when considering two normalized correspondences p^1 $ \hat{p}_{1} $ and p^2 $ \hat{p}_{2} $ , all the properties of fundamental matrix described in Section 8.3.1 are now applicable to essential matrix, the most useful of them being l2=Ep^1 $$ l_{2} = E\hat{p}_{1} $$ l1=ETp^2 $$ l_{1} = E^{T} \hat{p}_{2} $$ p^2TEp^1=0 $$ \hat{p}_{2} ^{T} E\hat{p}_{1} = 0 $$ p^1TETp^2=0. $$ \hat{p}_{1} ^{T} E^{T} \hat{p}_{2} = 0 . $$
Improving monocular visual SLAM in dynamic environments: an optical-flow-based approach
Published in Advanced Robotics, 2019
Jiyu Cheng, Yuxiang Sun, Max Q.-H. Meng
An overview of our proposed method is shown in Figure 2. There are two modules in the proposed method. The first module called Ego-motion Estimation is to estimate the camera ego-motion between two consecutive frames j and j−1. Our proposed method works as follows: Firstly, two consecutive images are captured and denoted as RGB Curr and RGB Last, as shown in Figure 2. We employ the Five-Point Algorithm [42] to estimate the motion of the camera from the last image to the current image by computing the essential matrix. Finally we multiply the last image with the estimated transformation matrix to get a new image which we call the estimated image. In this case, points in the estimated image are converted to the current image. The second module, Dynamic Feature Points Detection, calculates optical flow value for each feature point extracted from the current image between the current image and the estimated image and detects dynamic feature points for the current image based on optical flow values. Static points are used for further camera pose estimations.
A novel objective wrinkle evaluation method for printed fabrics based on multi-view stereo algorithm
Published in The Journal of The Textile Institute, 2022
Na Deng, Yiliang Wang, Binjie Xin, Wenzhen Wang
Where i denotes the camera number, j refers to the feature point number, zij stands for the image coordinates of the jth feature point of the ith camera, Pj indicates the spatial coordinate of jth point. By solving the least square problem, the optimal camera pose is obtained, then the values of essential matrix E and fundamental matrix F are calculated and the rotation matrix R and translation matrix t can be obtained by using matrix decomposition.