Soccer Robot Localization Detection Model Based on Monte Carlo Particle Filter and Template Matching Algorithm

With the continuous development of information technology, artificial intelligence and location detection technology have gradually penetrated various systems. The research progress in the field of humanoid robots is on paper, but there are still some defects that limit the performance of robots in some application scenarios. In order to solve the problem of feature recognition and robot self-localization in football field, this paper proposes a soccer robot localization detection model based on Monte Carlo particle filter and template matching algorithm. The model uses particle filter for robot positioning to achieve the purpose of global visual positioning and navigation, in order to meet the needs of real-time and accuracy during the competition. The image is preprocessed by template matching, and the feature information and edge information are extracted to recognize the target. The results show that the highest accuracy of the proposed algorithm is 0.895, and its accuracy is 0.99. When the recall rate reaches 1, the accuracy rate can still be maintained at 0.43, which verifies the effectiveness and practicability of the localization detection model under the use of this algorithm.