Calibration of a trinocular system formed with wide angle lens cameras

Parallel feature based calibration method for a trinocular vision sensor

In this paper, a new method to calibrate a trinocular vision sensor is presented. A planar target with several parallel lines is utilized. The trifocal tensor of three image planes can be calculated out according to line correspondences. Compatible essential matrix between each two cameras can be obtained. Then, rotation matrix and translation matrix can be deduced base on singular value decomposition of their corresponding essential matrix. In our proposed calibration method, image rectification is carried out to remove perspective distortion. As the feature utilized is straight line, precise point to point correspondence is not necessary. Experimental results show that our proposed calibration method can obtain precise results. Moreover, the trifocal tensor can also give a strict constraint for feature matching as descripted in our previous work. Root mean square error of measured distances is 0.029 mm with regards to the view field of about 250×250 mm. As parallel feature exists widely in natural scene, our calibration method also provides a new approach for self-calibration of a trinocular vision sensor.

Optical focal plane based on MEMS light lead-in for geometric camera calibration

The focal plane of a collimator used for the geometric calibration of an optical camera is a key element in the calibration process. The traditional focal plane of the collimator has only a single aperture light lead-in, resulting in a relatively unreliable calibration accuracy. Here we demonstrate a multi-aperture micro-electro-mechanical system (MEMS) light lead-in device that is located at the optical focal plane of the collimator used to calibrate the geometric distortion in cameras. Without additional volume or power consumption, the random errors of this calibration system are decreased by the multi-image matrix. With this new construction and a method for implementing the system, the reliability of high-accuracy calibration of optical cameras is guaranteed.

High-Accuracy Self-Calibration for Smart, Optical Orbiting Payloads Integrated with Attitude and Position Determination

A high-accuracy space smart payload integrated with attitude and position (SSPIAP) is a new type of optical remote sensor that can autonomously complete image positioning. Inner orientation parameters (IOPs) are a prerequisite for image position determination of an SSPIAP. The calibration of IOPs significantly influences the precision of image position determination of SSPIAPs. IOPs can be precisely measured and calibrated in a laboratory. However, they may drift to a significant degree because of vibrations during complicated launches and on-orbit functioning. Therefore, laboratory calibration methods are not suitable for on-orbit functioning. We propose an on-orbit self-calibration method for SSPIAPs. Our method is based on an auto-collimating dichroic filter combined with a micro-electro-mechanical system (MEMS) point-source focal plane. A MEMS procedure is used to manufacture a light transceiver focal plane, which integrates with point light sources and a complementary metal oxide semiconductor (CMOS) sensor. A dichroic filter is used to fabricate an auto-collimation light reflection element. The dichroic filter and the MEMS point light sources focal plane are integrated into an SSPIAP so it can perform integrated self-calibration. Experiments show that our method can achieve micrometer-level precision, which is good enough to complete real-time calibration without temporal or spatial limitations.

Non-iterative method for camera calibration

This paper presents a new and effective technique to calibrate a camera without nonlinear iteration optimization. To this end, the centre-of-distortion is accurately estimated firstly. Based on the radial distortion division model, point correspondences between model plane and its image were used to compute the homography and distortion coefficients afterwards. Once the homographies of calibration images are obtained, the camera intrinsic parameters are solved analytically. All the solution techniques applied in this calibration process are non-iterative that do not need any initial guess, with no risk of local minima. Moreover, estimation of the distortion coefficients and intrinsic parameters could be successfully decoupled, yielding the more stable and reliable result. Both simulative and real experiments have been carried out to show that the proposed method is reliable and effective. Without nonlinear iteration optimization, the proposed method is computationally efficient and can be applied to real-time online calibration.