Automated camera calibration and 3D egomotion estimation for augmented reality applications

Koller, Dieter; Klinker, Gudrun; Rose, E.; Breen, David E.; Whitaker, Ross T.; Tüceryan, Mihran

doi:10.1007/3-540-63460-6_118

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…Calibration of multiple camera systems became an important topic along with vision sensor networks (VSN) such as for: virtual and augmented reality; surveillance; battle field reconnaissance; etc. (Remagnino and Jones, 2002;Jaynes, 1999;Koller et al, 1997). In order to calibrate a VSN several critical steps must be taken: 1) acquiring images synchronously; 2) extracting feature points from the images; 3) establishing the correspondence among the feature points in multiple images from multiple cameras; 4) performing individual camera calibrations; and 5) computing a global coordinate reference for all cameras.…”

Section: Introductionmentioning

confidence: 99%

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Experimenting With Autonomous Calibration of a Camera Rig on a Vision Sensor Network

Dong

DeSouza

2010

Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics

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This paper presents a completely autonomous camera calibration framework for a vision sensor network consisting of a large number of arbitrarily arranged cameras. In the proposed framework, a sequence of images for calibration is collected without a tedious human intervention. Next, the system automatically extracts all necessary features from the images and finds the best set of images that minimizes the error in 3D reconstruction considering all cameras in the set.

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Section: Introductionmentioning

confidence: 99%

“…In (Olsen and Hoover, 2001), a system to calibrate cameras in a hallway was proposed using several square tiles. Similar to the landmarks in (Koller et al, 1997), their method not only requires that several tiles be carefully positioned, but also that the area covered by the tiles spans the field of view of all cameras in the hallway.…”

Section: Introductionmentioning

confidence: 99%

Experimenting With Autonomous Calibration of a Camera Rig on a Vision Sensor Network

Dong

DeSouza

2010

Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics

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“…Denzler and Zobel[1] used Kalman filter to estimate camera parameter with a selected focal length. Koller and Klinker [2] used an extended Kalman filter to estimate the camera location at any given time. Goddard and Abidi [3] use dual quaternion-based iterated extended Kalman filter to estimate relative 3D position and orientation (pose).…”

Section: Introductionmentioning

confidence: 99%

Recursive least squares approach to calculate motion parameters for a moving camera

Chang¹,

Fuller²,

Farsaie³

et al. 2003

SPIE Proceedings

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The increase in quality and the decrease in price of digital camera equipment have led to growing interest in reconstructing 3-Dimensional (3D) objects from sequences of 2-Dimensional (2D) images. The accuracy of the models obtained depends on two sets of parameter estimates. The first is the set of lens parameters -focal length, principal point, and distortion parameters. The second is the set of motion parameters that allows the comparison of a moving camera's desired location to a theoretical location.In this paper, we address the latter problem, i.e. the estimation of the set of 3D motion parameters from data obtained with a moving camera. We propose a method that uses Recursive Least Squares (RLS) for camera motion parameter estimation with observation noise. We accomplish this by calculation of hidden information through camera projection and minimization of the estimation error. We then show how a filter based on the motion parameters estimates may be designed to correct for the errors in the camera motion. The validity of the approach is illustrated by the presentation of experimental results obtained using the methods described in the paper.

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“…The challenges of visual registration as opposed to inertial, magnetic, or infrared systems is described in [6]. Real-time approaches using only visual input were presented by [7][8][9] and are based on pose estimation assuming known calibration. Another approach [10] relaxing the known calibration assumption performs only in a range where the affine projection model holds.…”

Section: Introductionmentioning

confidence: 99%

Visual and haptic collaborative tele-presence

Ansar

Rodrigues

Desai

et al. 2001

Computers & Graphics

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The core of a successful sense of presence is a visually, aurally, and haptically compelling experience. In this paper, we introduce the integration of vision and haptics for the purposes of remote collaboration. A remote station acquires a 3D-model of an object of interest which is transmitted to a local station. A user in the local station manipulates a virtual and the remote object as if he/she is haptically and visually at the remote station. This tele-presence feeling is achieved by visually registering the head-mounted display of the local user to the remote world and by dynamically registering the local object both visually and haptically with respect to the remote world. This can be achieved by adequate modeling and feedforward compensation including gravity compensation for the robotic manipulator with which the operator interacts. We present multiple scenarios where such a capability will be useful. One is remote design where a user tests a remotely designed docking station by inserting a virtual laptop into a model of the 3D docking station transmitted from a remote site. Medical robotics provides another possible scenario in which a resident is given surgical training to perform a virtual laparoscopy on a 3D exterior model of a patient, including tomographic registration of anatomical structures. We present results from numerous experiments from both the visual and haptic aspects as well as in integrated form. r

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Automated camera calibration and 3D egomotion estimation for augmented reality applications

Cited by 11 publications

References 31 publications

Experimenting With Autonomous Calibration of a Camera Rig on a Vision Sensor Network

Experimenting With Autonomous Calibration of a Camera Rig on a Vision Sensor Network

Recursive least squares approach to calculate motion parameters for a moving camera

Visual and haptic collaborative tele-presence

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