In this work, we propose a robust place recognition measurement in natural environments based on salient landmark screening and convolutional neural network (CNN) features. First, the salient objects in the image are segmented as candidate landmarks. Then, a category screening network is designed to remove specific object types that are not suitable for environmental modeling. Finally, a three-layer CNN is used to get highly representative features of the salient landmarks. In the similarity measurement, a Siamese network is chosen to calculate the similarity between images. Experiments were conducted on three challenging benchmark place recognition datasets and superior performance was achieved compared to other state-of-the-art methods, including FABMAP, SeqSLAM, SeqCNNSLAM, and PlaceCNN. Our method obtains the best results on the precision–recall curves, and the average precision reaches 78.43%, which is the best of the comparison methods. This demonstrates that the CNN features on the screened salient landmarks can be against a strong viewpoint and condition variations.

中文翻译：

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基于显着地标筛选和卷积神经网络特征的鲁棒位置识别

在这项工作中，我们提出了一种基于显着地标筛选和卷积神经网络 (CNN) 特征的自然环境中的鲁棒位置识别测量。首先，图像中的显着对象被分割为候选地标。然后，设计类别筛选网络以去除不适合环境建模的特定对象类型。最后，使用三层 CNN 来获得显着地标的高度代表性特征。在相似度测量中，选择了一个连体网络来计算图像之间的相似度。在三个具有挑战性的基准地点识别数据集上进行了实验，与其他最先进的方法（包括 FABMAP、SeqSLAM、SeqCNNSLAM 和 PlaceCNN）相比，实现了卓越的性能。我们的方法在精度-召回曲线上获得了最好的结果，平均精度达到了 78.43%，是比较方法中最好的。这表明筛选出的显着地标上的 CNN 特征可以对抗强烈的视点和条件变化。