Abstract
Three-Dimensional (3D) object pose estimation plays a crucial role in computer vision because it is an essential function in many practical applications. In this paper, we propose a real-time model-based object pose estimation algorithm, which integrates template matching and Perspective-n-Point (PnP) pose estimation methods to deal with this issue efficiently. The proposed method firstly extracts and matches keypoints of the scene image and the object reference image. Based on the matched keypoints, a two-dimensional (2D) planar transformation between the reference image and the detected object can be formulated by a homography matrix, which can initialize a template tracking algorithm efficiently. Based on the template tracking result, the correspondence between image features and control points of the Computer-Aided Design (CAD) model of the object can be determined efficiently, thus leading to a fast 3D pose tracking result. Finally, the 3D pose of the object with respect to the camera is estimated by a PnP solver based on the tracked 2D-3D correspondences, which improves the accuracy of the pose estimation. Experimental results show that the proposed method not only achieves real-time performance in tracking multiple objects, but also provides accurate pose estimation results. These advantages make the proposed method suitable for many practical applications, such as augmented reality.
Highlights
Accurate and efficient pose estimation of an Object-Of-Interest (OOI) is an important task in many robotic and computer vision applications involving vision-based robotic manipulation, position-based visual servoing, augmented reality, camera localization, etc
Keypoint matches between the scene and the model point clouds. Another category of model-based pose estimation methods commonly used in practice is Homography Decomposition (HD) methods that simplify the 3D model of the OOI as a planar model in the 3D workspace
In [21], Tjaden et al proposed a robust monocular pose estimation method based on temporally-consistent local color histograms, which can be used as statistical object descriptors within a template matching strategy for pose recovery in a cluttered environment
Summary
Accurate and efficient pose estimation of an Object-Of-Interest (OOI) is an important task in many robotic and computer vision applications involving vision-based robotic manipulation, position-based visual servoing, augmented reality, camera localization, etc. The authors in [11] proposed a model-based contour fitting algorithm, which estimates the optimal Another category of model-based pose estimation methods commonly used in practice is Homography Decomposition (HD) methods that simplify the 3D model of the OOI as a planar model in the 3D workspace. In [21], Tjaden et al proposed a robust monocular pose estimation method based on temporally-consistent local color histograms, which can be used as statistical object descriptors within a template matching strategy for pose recovery in a cluttered environment. It is possible for deep learning methods to reach the same purpose.
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