计算机技术与发展2024，Vol.34Issue(7)：131-137,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0128
基于独特性评价的特征点检测与视觉定位
Feature Point Detection and Visual Location Based on Distinctiveness Evaluation
摘要
Abstract
Traditional feature point detection algorithms are difficult to cope with the changes in lighting and viewpoint in the real scenes.The positioning accuracy of feature points obtained by the feature point detection algorithm based on deep learning is insufficient,and it is difficult to eliminate similar feature points in local areas.In order to improve the performance of algorithm based on deep learning,a feature point detection algorithm based on feature fusion and uniqueness evaluation is designed.Firstly,in order to improve the positioning accuracy of feature points,the network structure based on feature fusion and the corresponding feature fusion loss function are used to solve the problems of detail feature offset and blur in high-level features.Secondly,whether the feature points are from locally similar regions is converted into the uniqueness evaluation of the feature points,the uniqueness branch is added to the network structure,and the uniqueness loss function is designed to learn the uniqueness response value of the pixels in the predicted image.By extracting feature points with high uniqueness response values,the feature points of locally similar regions are excluded to reduce the number of mis-matched in subsequent feature matches.Based on the algorithm,the visual odometry and visual simultaneous localization and mapping system are constructed,and the system has good robustness and accurate positioning ability in large-scale outdoor scenes and small-scale indoor scenes on the KITTI and TUM datasets.关键词
人工智能/特征点检测/深度学习/视觉里程计/同时定位与构图Key words
artificial intelligence/feature point detection/deep learning/visual odometry/simultaneous localization and mapping分类
信息技术与安全科学引用本文复制引用
王欢,李创..基于独特性评价的特征点检测与视觉定位[J].计算机技术与发展,2024,34(7):131-137,7.基金项目
国家自然科学基金项目(U1901222) （U1901222）
广东省重点领域研发计划项目(2018B010112001) （2018B010112001）
