A Camera Pose Estimation Method for Rectangle Feature based Visual SLAM

2016 ◽  
Vol 11 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Jae-Min Lee ◽  
◽  
Gon-Woo Kim
Keyword(s):  
Pose Estimation ◽  
Estimation Method ◽  
Visual Slam ◽  
Camera Pose Estimation ◽  
Camera Pose ◽  
Feature Based

scholarly journals Voxel-Based Scene Representation for Camera Pose Estimation of a Single RGB Image

2020 ◽  
Vol 10 (24) ◽  
pp. 8866
Author(s):  
Sangyoon Lee ◽  
Hyunki Hong ◽  
Changkyoung Eem
Keyword(s):  
Pose Estimation ◽  
Estimation Method ◽  
Ground Truth ◽  
Training Dataset ◽  
Interest Points ◽  
Camera Pose Estimation ◽  
Polygonal Region ◽  
Camera Pose ◽  
End To End ◽  
Image Pairs

Deep learning has been utilized in end-to-end camera pose estimation. To improve the performance, we introduce a camera pose estimation method based on a 2D-3D matching scheme with two convolutional neural networks (CNNs). The scene is divided into voxels, whose size and number are computed according to the scene volume and the number of 3D points. We extract inlier points from the 3D point set in a voxel using random sample consensus (RANSAC)-based plane fitting to obtain a set of interest points consisting of a major plane. These points are subsequently reprojected onto the image using the ground truth camera pose, following which a polygonal region is identified in each voxel using the convex hull. We designed a training dataset for 2D–3D matching, consisting of inlier 3D points, correspondence across image pairs, and the voxel regions in the image. We trained the hierarchical learning structure with two CNNs on the dataset architecture to detect the voxel regions and obtain the location/description of the interest points. Following successful 2D–3D matching, the camera pose was estimated using n-point pose solver in RANSAC. The experiment results show that our method can estimate the camera pose more precisely than previous end-to-end estimators.

An onboard camera pose estimation method based on 3D digital surface model

2019 ◽  
Author(s):  
Xing Chang ◽  
Feng Li ◽  
Guiqin Yang ◽  
Yuhong Liu ◽  
Lei Xin ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Estimation Method ◽  
Surface Model ◽  
Digital Surface Model ◽  
Camera Pose Estimation ◽  
Camera Pose

New camera pose estimation method based on adaptive particle filter

2009 ◽  
Vol 28 (10) ◽  
pp. 2679-2682
Author(s):  
Wei LIU ◽  
Li-jun LI ◽  
Jun HAN ◽  
Tao GUAN
Keyword(s):  
Particle Filter ◽  
Pose Estimation ◽  
Estimation Method ◽  
Camera Pose Estimation ◽  
Camera Pose

Mirage: an O(n) time analytical solution to 3D camera pose estimation with multi-camera support

Robotica ◽  
2017 ◽  
Vol 35 (12) ◽  
pp. 2278-2296 ◽  
Author(s):  
Semih Dinc ◽  
Farbod Fahimi ◽  
Ramazan Aygun
Keyword(s):  
Pose Estimation ◽  
Visual Servoing ◽  
Comprehensive Evaluation ◽  
Linear Time ◽  
Estimation Method ◽  
Real Data ◽  
Camera Pose Estimation ◽  
Camera Systems ◽  
Camera Pose ◽  
Projection Error

SUMMARYMirage is a camera pose estimation method that analytically solves pose parameters in linear time for multi-camera systems. It utilizes a reference camera pose to calculate the pose by minimizing the 2D projection error between reference and actual pixel coordinates. Previously, Mirage has been successfully applied to trajectory tracking (visual servoing) problem. In this study, a comprehensive evaluation of Mirage is performed by particularly focusing on the area of camera pose estimation. Experiments have been performed using simulated and real data on noisy and noise-free environments. The results are compared with the state-of-the-art techniques. Mirage outperforms other methods by generating fast and accurate results in all tested environments.

Registration using adaptive particle filter and natural features matching techniques for augmented reality systems

2009 ◽  
Vol 29 (1) ◽  
pp. 75-84
Author(s):  
Guan Tao ◽  
Li Lijun ◽  
Liu Wei ◽  
Wang Cheng
Keyword(s):  
Augmented Reality ◽  
Particle Filter ◽  
Pose Estimation ◽  
Estimation Method ◽  
Scale Invariant ◽  
Registration Method ◽  
Content Type ◽  
Camera Pose Estimation ◽  
Camera Pose ◽  
Matching Techniques

PurposeThe purpose of this paper is to provide a flexible registration method for markerless augmented reality (AR) systems.Design/methodology/approachThe proposed method distinguishes itself as follows: firstly, the method is simple and efficient, as no man‐made markers are needed for both indoor and outdoor AR applications. Secondly, an adaptation method is presented to tune the particle filter dynamically. The result is a system which can achieve tolerance to fast motion and drift during tracking process. Thirdly, the authors use the reduced scale invariant feature transform (SIFT) and scale prediction techniques to match natural features. This method deals easily with the camera pose estimation problem in the case of large illumination and visual angle changes.FindingsSome experiments are provided to validate the performance of the proposed method.Originality/valueThe paper proposes a novel camera pose estimation method based on adaptive particle filter and natural features matching techniques.

On Camera Pose Estimation for 3D Scene Reconstruction

2020 ◽  
Author(s):  
Athul Thaliyachira Reji ◽  
Shashi Shekhar Jha ◽  
Ekta Singla
Keyword(s):  
Pose Estimation ◽  
Scene Reconstruction ◽  
Camera Pose Estimation ◽  
Camera Pose ◽  
3D Scene ◽  
3D Scene Reconstruction
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