A 3D Compensation Method for the Systematic Errors of Kinect V2

To reduce the 3D systematic error of the RGB-D camera and improve the measurement accuracy, this paper is the first to propose a new 3D compensation method for the systematic error of a Kinect V2 in a 3D calibration field. The processing of the method is as follows. First, the coordinate system between the RGB-D camera and 3D calibration field is transformed using 3D corresponding points. Second, the inliers are obtained using the Bayes SAmple Consensus (BaySAC) algorithm to eliminate gross errors (i.e., outliers). Third, the parameters of the 3D registration model are calculated by the iteration method with variable weights that can further control the error. Fourth, three systematic error compensation models are established and solved by the stepwise regression method. Finally, the optimal model is selected to calibrate the RGB-D camera. The experimental results show the following: (1) the BaySAC algorithm can effectively eliminate gross errors; (2) the iteration method with variable weights could better control slightly larger accidental errors; and (3) the 3D compensation method can compensate 91.19% and 61.58% of the systematic error of the RGB-D camera in the depth and 3D directions, respectively, in the 3D control field, which is superior to the 2D compensation method. The proposed method can control three types of errors (i.e., gross errors, accidental errors and systematic errors) and model errors and can effectively improve the accuracy of depth data.

Research on the Optimization Model of the Abrasive Blocks Using Weighted Case-Based Reasoning

Barrel finishing process is a universal method to improve the surface quality of parts. It is widely used in high-performance parts of high-end equipment. As a necessary tool consumable for barrel finishing process, the characteristic parameters of the abrasive blocks affect the processing quality and production efficiency. However, the current method for selecting the abrasive blocks requires large number of experiments based on the operator’s extensive experience, which does not meet the rapid development needs of the barrel finishing process. Therefore, this paper proposes a case-based reasoning model with variable weights to achieve the intelligent optimization of the abrasive blocks. Based on the in-depth analysis of the characteristics of the barrel finishing process, a reasonable case base is established firstly, which is to determine the comprehensive case features and the solution of the case. AHP (analytic hierarchy process) is proposed to determine the weight of case features and to dynamically adjust the weight of case features according to the characteristics of the parts to be processed and users’ processing requirements. The results show that the proposed case-based reasoning model with variable weights can quickly, accurately, and reasonably select the abrasive blocks during the process of making processing technique of the barrel finishing, which will lay a necessary foundation for the effective implementation of the barrel finishing process and contribute significantly to the improvement of its efficiency.