Abstract
In order to improve the real-time performance of visual positioning of the indoor mobile robot, the researchers found that the shape and size of the positioned image have a great influence on the real-time performance of the positioning calculation. In order to verify the conclusion and find the appropriate image shape and size to meet the robot’s visual positioning requirements, this paper adopts four different shapes, such as quadrilateral and circular, and uses SURF algorithm to extract and recognize the features of the image. The effect of image shape and size on real-time localization is studied from two aspects: the localization of different shape models under the same size by the visual robot and the localization of the different shape models by the visual robot. It is found that the accuracy and real time of positioning squares and circles are higher than the accuracy and real time of positioning triangles and hexagons under the same size. And when the image size ratio is between 40 and 60% of the original image, the change of the number of feature points is relatively stable and the number of feature points is moderate. It can improve the real-time performance of mobile robot vision localization under the premise of a certain positioning accuracy.
Highlights
As a high-end electromechanical device integrating a computer system, a control system, a sensing system, a mechanical system, and an electrical system, the robot has a high degree of complexity
The research shows that the location of the feature points extracted by Scale invariant features transform (SIFT) is accurate, which has good affine, light invariance, and high real-time performance, and the overall performance is higher than other local feature extraction operators
In order to verify the conclusion that the image shape and image size have a great influence on the real-time positioning and find the appropriate image shape and size to meet the robot’s visual positioning requirements, in this paper, four different shapes such as normal quadrilateral are used, and the Speeded-Up Robust Features (SURF) algorithm is used to study the influence of image shape and size on the real-time positioning of the visual robot from the positioning of various shape models under the same size and the positioning of the visual robot to the same shape and different size models
Summary
As a high-end electromechanical device integrating a computer system, a control system, a sensing system, a mechanical system, and an electrical system, the robot has a high degree of complexity. The research shows that the location of the feature points extracted by SIFT is accurate, which has good affine, light invariance, and high real-time performance, and the overall performance is higher than other local feature extraction operators. SIFT algorithm has achieved great success in the field of target recognition and image matching. When indoor mobile robot is positioned autonomously, it requires high real-time visual localization, but the accuracy of image recognition is not high. Document [9] proposed an improved feature descriptor, RIBRIEF, to improve the overall real-time performance of the algorithm by combining descriptor index with descriptor clustering, based on fast stable feature point extraction and logical computation similarity. The experimental results show that compared with descriptor BRIEF and SURF algorithm, the image matching algorithm based on RIBRIEF has obvious advantages in robust real-time performance. In order to verify the conclusion that the image shape and image size have a great influence on the real-time positioning and find the appropriate image shape and size to meet the robot’s visual positioning requirements, in this paper, four different shapes such as normal quadrilateral are used, and the SURF algorithm is used to study the influence of image shape and size on the real-time positioning of the visual robot from the positioning of various shape models under the same size and the positioning of the visual robot to the same shape and different size models
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