Binocular stereovision omnidirectional motion handling robot

Binocular stereovision has become one of the development trends of machine vision and has been widely used in robot recognition and positioning. However, the current research on omnidirectional motion handling robots at home and abroad is too limited, and many problems cannot be solved well, such as single operating systems, complex algorithms, and low recognition rates. To make a high-efficiency handling robot with high recognition rate, this article studies the problem of robot image feature extraction and matching and proposes an improved speeded up robust features (SURF) algorithm that combines the advantages of both SURF and Binary Robust Independent Elementary Features. The algorithm greatly simplifies the complexity of the algorithm. Experiments show that the improved algorithm greatly improves the speed of matching and ensures the real-time and robustness of the algorithm. In this article, the problem of positioning the target workpiece of the robot is studied. The three-dimensional (3-D) reconstruction of the target workpiece position is performed to obtain the 3-D coordinates of the target workpiece position, thereby completing the positioning work. This article designs a software framework for real-time 3-D object reconstruction. A Bayesian-based matching algorithm combined with Delaunay triangulation is used to obtain the relationship between supported and nonsupported points, and 3-D reconstruction of target objects from sparse to dense matches is achieved.

A High Reliability 3D Scanning Measurement of the Complex Shape Rail Surface of the Electromagnetic Launcher

After an electromagnetic railgun launch, a series of damage phenomena may cause the inner bore surface to become complex, such as gouging and deposition. Furthermore, the rail surface will be uneven and blackened by oxidation. To understand these forms of rail degradation, many previous studies have mentioned several surface scanning methods, but none of these can be used in the complex inner bore. Therefore, we present a 3D scanning system based on binocular stereovision technology combined with the active illumination, which can be used to obtain the rail surface topography under a complex inner bore environment. The laser dot projection is applied as the active illumination. In contrast with other active illumination, laser dot projection has high reconstruction reliability. By combining laser dot projection with binocular stereovision, the object can be completely reconstructed. In addition, an image acquisition method which can improve image signal-to-noise ratio is proposed. The proof-of-principle experiment of the system is done under dim light conditions. Through the experiment, the 3D depth map of the rail surface is obtained and the gouge crater is scanned out. Meanwhile, system evaluation and measurement uncertainty analysis have also been carried out.

Novel and Automatic Rice Thickness Extraction Based on Photogrammetry Using Rice Edge Features

The dimensions of phenotyping parameters such as the thickness of rice play an important role in rice quality assessment and phenotyping research. The objective of this study was to propose an automatic method for extracting rice thickness. This method was based on the principle of binocular stereovision but avoiding the problem that it was difficult to directly match the corresponding points for 3D reconstruction due to the lack of texture of rice. Firstly, the shape features of edge, instead of texture, was used to match the corresponding points of the rice edge. Secondly, the height of the rice edge was obtained by way of space intersection. Finally, the thickness of rice was extracted based on the assumption that the average height of the edges of multiple rice is half of the thickness of rice. According to the results of the experiments on six kinds of rice or grain, errors of thickness extraction were no more than the upper limit of 0.1 mm specified in the national industry standard. The results proved that edge features could be used to extract rice thickness and validated the effectiveness of the thickness extraction algorithm we proposed, which provided technical support for the extraction of phenotyping parameters for crop researchers.