Camera calibration is a crucial prerequisite in many applications of computer vision. In this paper, a new geometry-based camera calibration technique is proposed, which resolves two main issues associated with the wi...
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Camera calibration is a crucial prerequisite in many applications of computer vision. In this paper, a new geometry-based camera calibration technique is proposed, which resolves two main issues associated with the widely used Zhang's method: (i) the lack of guidelines to avoid outliers in the computation and (ii) the assumption of fixed camera focal length. The proposed approach is based on the closed-form solution of principal lines with their intersection being the principal point while each principal line can concisely represent relative orientation/position (up to one degree of freedom for both) between a special pair of coordinate systems of image plane and calibration pattern. With such analytically tractable image features, computations associated with the calibration are greatly simplified, while the guidelines in (i) can be established intuitively. Experimental results for synthetic and real data show that the proposed approach does compare favorably with Zhang's method, in terms of correctness, robustness, and flexibility, and addresses issues (i) and (ii) satisfactorily.
Visible light positioning (VLP) is a promising technology since it can provide high-accuracy indoor localization based on the existing lighting infrastructure. Most VLP systems require a prior light-emitting diode (LE...
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Visible light positioning (VLP) is a promising technology since it can provide high-accuracy indoor localization based on the existing lighting infrastructure. Most VLP systems require a prior light-emitting diode (LED) location map, termed a VLP-landmark map in this article, for which manual surveys are costly in practical deployment at scale. What is more, the existing approaches also require dense LED deployments. In this work, we proposed a multisensor fusion framework, termed VWR-simultaneous localization and mapping (SLAM), which tightly fused the VLP, wheel odometer, and red green dlue-depth map (RGB-D) camera to achieve SLAM. Our VWR-SLAM can provide accurate and robust robot localization and navigation in LED shortage/outage situations, meanwhile, constructing the 3-D sparse environment map and the 3-D VLP-landmark map without tedious manual measurements. The experimental results show that our proposed scheme can provide an average robot positioning accuracy of 1.81 cm and an LED mapping accuracy of 3.01 cm.
In this study, the authors study lateral and longitudinal road slope estimation, which are important parameters to control a vehicle laterally in lane-keeping assistance and longitudinally in adaptive cruise control, ...
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In this study, the authors study lateral and longitudinal road slope estimation, which are important parameters to control a vehicle laterally in lane-keeping assistance and longitudinally in adaptive cruise control, respectively. They present three methods estimating road slopes from instant road images obtained from a front monocular camera. In the first geometry-based (GB) method, they obtain the slope estimates using 2D road lines derived from 3D road line and pin-hole camera models. In the second local features-based (LFB) method, they use the SIFT local features between two consecutive image pairs. In the first two methods, they derive computationally simple novel analytic expressions for the road slope estimates. As a third solution in covariance based (CB) method, they consider supervised learning where they use 2D road lines as features to train their algorithm. GB and CB methods rely on road lines and provide in advance estimations that enable proactive driving, whereas LFB uses the entire image and performs estimation when the road slope actually changes. They use multi-layer perceptron learning method as a benchmark in their tests. They compare the estimation performance, system requirements, and applicable conditions of the proposed methods under controlled and uncontrolled test environments.
A geometry-based method is proposed in this paper to solve the stereo-correspondence problem of single-lens biprism stereovision *** an optical bi-prism and a static CCD camera setup,image pairs are captured synchrono...
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A geometry-based method is proposed in this paper to solve the stereo-correspondence problem of single-lens biprism stereovision *** an optical bi-prism and a static CCD camera setup,image pairs are captured synchronously by two virtual cameras which are defined by two boundary lines:optical axis and CCD camera field of view(FOV) boundary *** geometrical relationship between the 2D images and the 3D scene are established by applying two fundamental theories:geometrical analysis in which all the pertinent points,lines and planes are expressed in 3D Camera Coordinates and the Rules of Refraction. Subsequently,the least square method is employed to fit the epipolar line with a set of obtained corresponding point candidates. The corresponding points are located by searching along the epipolar fine with SSD(Sum of Square Difference) correlation algorithm. By utilizing the uniquely geometrical properties of the bi-prism stereovision system,complicated camera calibration procedures are exempted to settle down the stereo *** geometry-based method can be easily generalized to tri-ocular and multi-ocular stereovision systems to facilitate the solving of stereo-correspondence *** are conducted to validate this method.
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