Recently, the methods based on Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in visual place recognition. CNN is a class of multilayer perceptrons, but unlike common multilayer perceptrons that it is not usually fully connected networks. It can acquire more general image features and make the image processing computationally manageable through filtering the connections by proximity. In this paper, we utilize the deep features generated by CNNs and the dynamic time warping (DTW) algorithm for image sequence place recognition. We propose a novel image similarity measurement, which is derived from cosine distance and can better distinguish match and mismatch. Meanwhile, we improve the DTW algorithm to design a local matching method that can reduce time complexity from O(n³) to O(n). To test the proposed method, four datasets (Nordland, Gardens Point, St. Lucia, and UoA datasets) are used as benchmarks; using two traverses in each dataset with one for reference and the other for testing. The results show high precision-recall characteristics of our method in the cases of severe appearance changes. Besides, our method achieves substantial improvements over the methods using the deep feature representations of a single image for recognition, which reflects that the spatiotemporal information contained in the image sequence is significant for the task of visual place recognition. Moreover, the proposed method also shows to outperform the classical sequence-based method SeqSLAM.

中文翻译：

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使用改进的 DTW 在不断变化的环境中实现强大的视觉位置识别

最近，基于卷积神经网络 (CNN) 的方法在视觉位置识别方面取得了最先进的性能。CNN 是一类多层感知器，但与常见的多层感知器不同的是，它通常不是全连接网络。它可以通过接近度过滤连接来获取更一般的图像特征并使图像处理在计算上易于管理。在本文中，我们利用 CNN 生成的深度特征和动态时间规整 (DTW) 算法进行图像序列位置识别。我们提出了一种新颖的图像相似度测量方法，它源自余弦距离，可以更好地区分匹配和不匹配。同时，我们改进了DTW算法，设计了一种可以降低时间复杂度的局部匹配方法。○(n³ ) 到○(n）。为了测试所提出的方法，使用四个数据集（Nordland、Gardens Point、St. Lucia 和 UoA 数据集）作为基准；在每个数据集中使用两个遍历，一个用于参考，另一个用于测试。结果表明，我们的方法在外观发生严重变化的情况下具有高精度的召回特性。此外，我们的方法比使用单个图像的深度特征表示进行识别的方法取得了实质性的改进，这反映了图像序列中包含的时空信息对于视觉位置识别的任务非常重要。此外，所提出的方法还显示出优于经典的基于序列的方法 SeqSLAM。