Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).

/pdf/pose-estimation-of-a-mobile-robot-based-on-fusion-of-imu-44r71oehjx.pdf

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter.

Single-camera stereo-digital image correlation (stereo-DIC) techniques have gained increasing attentions and demonstrated excellent prospects in the experimental mechanics community owing to their prominent advantages of cost-effectiveness, compactness, and the avoidance of the complicated camera synchronization. Using additional optical devices, e.g. a diffraction grating, a bi-prism or a set of planar mirrors, pseudo stereo images of a test sample surface can be recorded with a single camera. By correlating these stereo images using DIC, full-field three-dimensional (3D) shape and deformation can be retrieved. This review comprehensively summarizes the historical development, methodologies, strengths and weaknesses of the diffraction grating-based, prism-based, four-mirror-adaptor-based single-camera stereo-DIC techniques, and the recently proposed novel full-frame single color camera-based stereo-DIC technique for full-field 3D shape and deformation measurement. The optical arrangements, principles and calibration procedures of these single-camera stereo-DIC techniques are described in detail. Since high-speed deformation measurement is efficiently achieved by combining the single-camera stereo-DIC with one high-speed camera, single-camera stereo-DIC techniques show great potential in impact engineering, vibration and other dynamic tests.

Review of single-camera stereo-digital image correlation techniques for full-field 3D shape and deformation measurement

Binocular vision calibration is of great importance in 3D machine vision measurement. With respect to binocular vision calibration, the nonlinear optimization technique is a crucial step to improve the accuracy. The existing optimization methods mostly aim at minimizing the sum of reprojection errors for two cameras based on respective 2D image pixels coordinate. However, the subsequent measurement process is conducted in 3D coordinate system which is not consistent with the optimization coordinate system. Moreover, the error criterion with respect to optimization and measurement is different. The equal pixel distance error in 2D image plane leads to diverse 3D metric distance error at different position before the camera. To address these issues, we propose a precise calibration method for binocular vision system which is devoted to minimizing the metric distance error between the reconstructed point through optimal triangulation and the ground truth in 3D measurement coordinate system. In addition, the inherent epipolar constraint and constant distance constraint are combined to enhance the optimization process. To evaluate the performance of the proposed method, both simulative and real experiments have been carried out and the results show that the proposed method is reliable and efficient to improve measurement accuracy compared with conventional method.

Precise calibration of binocular vision system used for vision measurement

Cholesteric liquid crystals (CLCs) have a photonic bandgap due to the periodic change of refractive index along their helical axes. The CLCs containing optical gain have served as band-edge lasing resonators. In particular, CLCs in a granular format provide omnidirectional lasing, which are promising as a point light source. However, there is no platform that simultaneously achieves high stability in air and wavelength tunability. We encapsulate CLCs with double shells to design a capsule-type laser resonator. The fluidic CLCs are fully enclosed by an aqueous inner shell that promotes the planar alignment of LC molecules along the interface. The outer shell made of silicone elastomer protects the CLC core and the inner shell from the surroundings. Therefore, the helical axes of the CLCs are radially oriented within the capsules, which provide a stable omnidirectional lasing in the air. At the same time, the fluidic CLCs enable the fine-tuning of lasing wavelength with temperature. The capsules retain their double-shell structure during the dynamic deformation. Therefore, the CLCs in the core maintain the planar alignment along the deformed interface, and a lasing direction can be varied from omnidirectional to bi- or multidirectional, depending on the shape of deformed capsules.

https://advances.sciencemag.org/content/advances/4/6/eaat8276.full.pdf

Wavelength-tunable and shape-reconfigurable photonic capsule resonators containing cholesteric liquid crystals

Research on multi-camera calibration and point cloud correction method based on three-dimensional calibration object

Camera calibration plays an important role in the field of machine vision applications. During the process of camera calibration, nonlinear optimization technique is crucial to obtain the best performance of camera parameters. Currently, the existing optimization method aims at minimizing the distance error between the detected image point and the calculated back-projected image point, based on 2D image pixels coordinate. However, the vision measurement process is conducted in 3D space while the optimization method generally adopted is carried out in 2D image plane. Moreover, the error criterion with respect to optimization and measurement is different. In other words, the equal pixel distance error in 2D image plane leads to diverse 3D metric distance error at different position before the camera. All the reasons mentioned above will cause accuracy decrease for 3D vision measurement. To solve the problem, a novel optimization method of camera parameters used for vision measurement is proposed. The presented method is devoted to minimizing the metric distance error between the calculated point and the real point in 3D measurement coordinate system. Comparatively, the initial camera parameters acquired through linear calibration are optimized through two different methods: one is the conventional method and the other is the novel method presented by this paper. Also, the calibration accuracy and measurement accuracy of the parameters obtained by the two methods are thoroughly analyzed and the choice of a suitable accuracy evaluation method is discussed. Simulative and real experiments to estimate the performance of the proposed method on test data are reported, and the results show that the proposed 3D optimization method is quite efficient to improve measurement accuracy compared with traditional method. It can meet the practical requirement of high precision in 3D vision metrology engineering.

A novel optimization method of camera parameters used for vision measurement

The combination of omnidirectional imaging and structured-light vision technique provides an efficient solution for omnidirectional 3D measurement. However, the existing omnidirectional structured-light vision sensors are constructed on the basis of curved mirrors, such as parabolic mirrors and conic mirrors, which inevitably generate two obvious problems: first, the difficulty of camera calibration is undoubtedly increased because the camera mathematical model becomes nonlinear and the traditional camera model is no longer applicable in those cases and second, the measurement accuracies of those sensors are far worse than those of traditional structured-light sensors since the captured images are severely warp-deformed. To solve these problems, we developed a novel omnidirectional laser vision sensor by adopting plane mirrors instead of curved mirrors so that the simple traditional camera model can still be used, and more important, the measurement accuracy can be maintained at the same level with the traditional structured-light vision. With respect to the sensor designing, the omnidirectional camera is constructed by a traditional perspective camera and a pyramid reflector, which is made up of four identical plane mirrors of isosceles triangular shape. Moreover, four laser projectors are used to project structured-light to the surrounding scene by the way of splicing. The sensor is calibrated by a 2D planar target and a quasi-3D target. The experimental results show that the relative calibration and measurement errors of the sensor are less than 0.08% and 0.16% respectively. It is proved that complete omnidirectional 3D measurement with high accuracy is feasible with the proposed sensor system.

Yexin Wang

Papers

Precise calibration of binocular vision system used for vision measurement

A novel optimization method of camera parameters used for vision measurement

A novel laser vision sensor for omnidirectional 3D measurement

Line-based camera calibration with lens distortion correction from a single image

Accurate and robust estimation of camera parameters using RANSAC