为实现机器人识别导航中图像的准确匹配，提出一种以新型深度神经网络模型作为检测器进行匹配特征点检测的方法。根据数据集搭建深度神经网络模型实现从图像到特征点概率图的映射；将得到的特征图通过非极大抑制提取关键点的位置；最后根据检测到的关键点进行准确匹配，找到最为匹配的图像。实验表明，通过该方法检测到的关键点匹配率高，且通过匹配特征点可以实现准确的图像校正。相比传统的图像匹配方法，深度神经网络模型作为检测器优势显著。 In order to achieve accurate image matching in robot recognition navigation, a new method for detecting matching feature points using a new deep neural network as a detector is proposed. Build a deep neural network model based on the data set to implement the mapping from the image to the feature point probability map. Then extract the position of the key feature points through non-maximum suppression of the obtained feature map. Finally, match the detected key feature points accurately to find the most matching image. Experiments show that the method of key feature points detected by deep neural network can make the matching rate high, and further ex-periments have found that it can also achieve accurate image correction. Compared with the traditional method, the method of deep neural network as a detector has significant advantages.
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机译：为实现机器人识别导航中图像的准确匹配，提出一种以新型深度神经网络模型作为检测器进行匹配特征点检测的方法。根据数据集搭建深度神经网络模型实现从图像到特征点概率图的映射;将得到的特征图通过非极大抑制提取关键点的位置;最后根据检测到的关键点进行准确匹配，找到最为匹配的图像。实验表明，通过该方法检测到的关键点匹配率高，且通过匹配特征点可以实现准确的图像校正。相比传统的图像匹配方法，深度神经网络模型作为检测器优势显着。 In order to achieve accurate image matching in robot recognition navigation, a new method for detecting matching feature points using a new deep neural network as a detector is proposed. Build a deep neural network model based on the data set to implement the mapping from the image to the feature point probability map. Then extract the position of the key feature points through non-maximum suppression of the obtained feature map. Finally, match the detected key feature points accurately to find the most matching image. Experiments show that the method of key feature points detected by deep neural network can make the matching rate high, and further ex-periments have found that it can also achieve accurate image correction. Compared with the traditional method, the method of deep neural network as a detector has significant advantages.
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