Seyyed A. Hoseini; P. Kabiri
Abstract
When a camera moves in an unfamiliar environment, for many computer vision and robotic applications it is desirable to estimate camera position and orientation. Camera tracking is perhaps the most challenging part of Visual Simultaneous Localization and Mapping (Visual SLAM) and Augmented Reality problems. ...
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When a camera moves in an unfamiliar environment, for many computer vision and robotic applications it is desirable to estimate camera position and orientation. Camera tracking is perhaps the most challenging part of Visual Simultaneous Localization and Mapping (Visual SLAM) and Augmented Reality problems. This paper proposes a feature-based approach for tracking a hand-held camera that moves within an indoor place with a maximum depth of around 4-5 meters. In the first few frames the camera observes a chessboard as a marker to bootstrap the system and construct the initial map. Thereafter, upon arrival of each new frame, the algorithm pursues the camera tracking procedure. This procedure is carried-out in a framework, which operates using only the extracted visible natural feature points and the initial map. Constructed initial map is extended as the camera explores new areas. In addition, the proposed system employs a hierarchical method on basis of Lucas-Kanade registration technique to track FAST features. For each incoming frame, 6-DOF camera pose parameters are estimated using an Unscented Kalman Filter (UKF). The proposed algorithm is tested on real-world videos and performance of the UKF is compared against other camera tracking methods. Two evaluation criteria (i.e. Relative pose error and absolute trajectory error) are used to assess performance of the proposed algorithm. Accordingly, reported experimental results show accuracy and effectiveness and of the presented approach. Conducted experiments also indicate that the type of extracted feature points has not significant effect on precision of the proposed approach.
H.3.2.2. Computer vision
Seyyed A. Hoseini; P. Kabiri
Abstract
In this paper, a feature-based technique for the camera pose estimation in a sequence of wide-baseline images has been proposed. Camera pose estimation is an important issue in many computer vision and robotics applications, such as, augmented reality and visual SLAM. The proposed method can track captured ...
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In this paper, a feature-based technique for the camera pose estimation in a sequence of wide-baseline images has been proposed. Camera pose estimation is an important issue in many computer vision and robotics applications, such as, augmented reality and visual SLAM. The proposed method can track captured images taken by hand-held camera in room-sized workspaces with maximum scene depth of 3-4 meters. The system can be used in unknown environments with no additional information available from the outside world except in the first two images that are used for initialization. Pose estimation is performed using only natural feature points extracted and matched in successive images. In wide-baseline images unlike consecutive frames of a video stream, displacement of the feature points in consecutive images is notable and hence cannot be traced easily using patch-based methods. To handle this problem, a hybrid strategy is employed to obtain accurate feature correspondences. In this strategy, first initial feature correspondences are found using similarity of their descriptors and then outlier matchings are removed by applying RANSAC algorithm. Further, to provide a set of required feature matchings a mechanism based on sidelong result of robust estimator was employed. The proposed method is applied on indoor real data with images in VGA quality (640×480 pixels) and on average the translation error of camera pose is less than 2 cm which indicates the effectiveness and accuracy of the proposed approach.
