Calibration Of Intrinsic Parameters Research Articles

Correction method for non-landing measuring of vehicle-mounted theodolite based on static datum conversion

During the non-landing measuring of vehicle mounted theodolite, especially under high-speed tracking measurement, the misalignment of theodolite’s center of mass and spindle etc. will cause high-frequency vibration of theodolite platform, increase the observation error of targets and even unbelievable results. In this paper, a correction method of non-landing measuring of theodolite based on static datum conversion is presented, which can effectively improve the observation accuracy of theodolite. The CCD camera is fixed to the theodolite platform to calculate the gesture shaking quantity of theodolite platform in geodetic coordinate system through the real time imaging of static datum. The observation results of theodolite are corrected by using such shaking quantity. The experiment shows that the correction accuracy exceeds 10 s of arc. The intrinsic parameter calibration technology of camera based on stellar angular distance and absolute conic put forward in this paper can prevent the estimated error of extrinsic parameters influencing the intrinsic parameter calibration and improve the intrinsic parameter calibration accuracy; the static datum conversion technology can reduce the influence of installation error of camera and theodolite platform on gesture measuring of the platform. The simulation experiment shows that when the shaking range of the platform is less than 30 min of arc, the influence of the three-axis installation error of camera within 3deg on the accuracy of correction results is less than 8 s of arc. The method in this paper can be extended to and used in the field of gesture shaking measuring and micro-structure deformation of various unstable platforms, therefore it is of important theoretical research significance and has wide engineering application prospect.

IMU Self-Calibration Using Factorization

This paper presents a convenient self-calibration method for an inertial measurement unit (IMU) using matrix factorization. Using limited information about applied loads (accelerations or angular rates) available from natural references, the proposed method can linearly solve all the parameters of an IMU in any configuration of its inertial components. Our factorization-based calibration method exploits the bilinear form of an IMU measurement, which is the product of intrinsic calibration parameters and exerted loads. For a redundant IMU, we prove that partial knowledge of the loads, such as magnitude, can produce a linear solution space for a proper decomposition of the measurement. Theoretical analysis on this linear space reveals that a 1-D null space should be considered when load magnitudes are all equal (e.g., gravity loads). Degenerate load distributions are also geometrically identified to avoid singular measurement collection. Since a triad IMU has a lower number of sensor components than a 4-D parameter space, we propose an iterative factorization in which only initial bias is required. A wide convergence region of the bias can provide an automatic setting of the initial bias as the mean of the measurements. Performance of the proposed method is evaluated with respect to various noise levels and constraint types. Self-calibration capability is demonstrated using natural references, which are gravity for accelerometers and image stream from an attached camera for gyroscopes. Calibration results are globally optimal and identical to those of nonlinear optimization.

Self-calibration for a 3D laser

In this paper we describe a method for the automatic self-calibration of a 3D laser sensor. We wish to acquire crisp point clouds and so we adopt a measure of crispness to capture point cloud quality. We then pose the calibration problem as the task of maximizing point cloud quality. Concretely, we use Rényi Quadratic Entropy to measure the degree of organization of a point cloud. By expressing this quantity as a function of key unknown system parameters, we are able to deduce a full calibration of the sensor via an online optimization. Beyond details on the sensor design itself, we fully describe the end-to-end intrinsic parameter calibration process and the estimation of the clock skews between the constituent microprocessors. We analyse performance using real and simulated data and demonstrate robust performance over 30 test sites.

Microassembly of micropeg and -hole using uncalibrated visual servoing method

In order to achieve high performance visual servoing, substantial efforts must be made for the calibration of intrinsic parameters of the camera and the transformation matrix as well. In order to avoid the tedious and difficult calibration work, a Kalman filtering-based estimation algorithm is proposed to estimate the composite image Jacobian or the homogeneous transformation matrix on-line, which can reduce the influence of noise. Using the estimated Jacobian matrix, a PD visual controller is used to make features converge to desired values with satisfactory dynamic performance, without a priori knowledge of the kinematic structure and system parameters. The proportional and differential gains are tuned using a genetic algorithm to obtain optimal controller parameters. A series of experiments are performed on peg and hole assembly to investigate the feasibility and effectiveness of this method.

Kinematic calibration of an active head-eye system

Usually, a head-eye system comprises of a pair of cameras mounted on a platform. Calibration of such a system can be divided into two parts. The first part is concerned with calibration of intrinsic parameters of the cameras where as the second part deals with calibration of extrinsic parameters of the cameras which is realized through kinematic calibration of the system. In this paper, we solve this kinematic calibration problem. First we formulate the problem for a 6-degree-of-freedom (DOF) head-eye system. It turns out that this problem is very similar to the hand-eye calibration problem, i.e., to solve an equation system of AX=XB, where X is the unknown transformation matrix which contains a rotation and a translation. In a special case, where the system has only rotational motion, the rotation and translation of X are decomposed into two independent equations which are solved separately. We propose a nonlinear optimization solution for the rotation. Algorithms from early work have also be implemented for the purpose of comparison. Experiments and tests are performed on both synthetic and real data. Results are compared and presented in this paper.

Intrinsic parameter calibration procedure for a (high-distortion) fish-eye lens camera with distortion model and accuracy estimation*

This paper presents a calibration procedure for a fish-eye lens (a high-distortion lens) mounted on a CCD TV camera. The method is designed to account for the differences in images acquired via a distortion-free lens camera setup and the images obtained by a fish-eye lens camera. The calibration procedure essentially defines a mapping between points in the world coordinate system and their corresponding point locations in the image plane. This step is important for applications in computer vision which involve quantitative measurements. The objective of this mapping is to estimate the internal parameters of the camera, including the effective focal length, one-pixel width on the image plane, image distortion center, and distortion coefficients. The number of parameters to be calibrated is reduced by using a calibration pattern with equally spaced dots and assuming a pin-hole model camera behavior for the image center, thus assuming negligible distortion at the image distortion center. Our method employs a non-linear transformation between points in the world coordinate system and their corresponding location on the image plane. A Lagrangian minimization method is used to determine the coefficients of the transformation. The validity and effectiveness of our calibration and distortion correction procedure are confirmed by application of this procedure on real images.