论文标题
使用深层卷积自动编码器，在视觉猛击中进行自我监督的矢量定量
Self-supervised Vector-Quantization in Visual SLAM using Deep Convolutional Autoencoders
论文作者
论文摘要
在本文中，我们介绍了AE-fabmap，这是一种新的基于单词的SLAM方法的新型自制袋。我们还提出了AE-ORB-SLAM，这是基于弓的路径计划算法的当前状态的修改版本。也就是说，我们已经使用了深度卷积自动编码器来查找循环封闭。在单词袋的情况下，矢量量化（VQ）被认为是SLAM过程中最耗时的部分，通常使用无处可比的算法（例如Kmeans ++）在SLAM算法的离线阶段执行。我们已经通过集成用于进行矢量量化的自动编码器来以自我监督的方式解决了基于弓的SLAM方法的循环闭合检测部分。这种方法可以提高大规模大规模的精度，那里有大量未标记的数据。使用自我监督的主要优点是它可以帮助减少标签量。此外，实验表明，在速度和记忆消耗方面，自动编码器比诸如图形卷积神经网络（例如图形卷积神经网络）的效率要高得多。我们将这种方法集成到了最先进的远程外观词汇袋Fabmap2的视觉袋中。在所有情况下，实验证明了这种方法在室内和室外数据集中比常规FABMAP2的优越性，并且在环路闭合检测和轨迹产生方面的准确性更高。
In this paper, we introduce AE-FABMAP, a new self-supervised bag of words-based SLAM method. We also present AE-ORB-SLAM, a modified version of the current state of the art BoW-based path planning algorithm. That is, we have used a deep convolutional autoencoder to find loop closures. In the context of bag of words visual SLAM, vector quantization (VQ) is considered as the most time-consuming part of the SLAM procedure, which is usually performed in the offline phase of the SLAM algorithm using unsupervised algorithms such as Kmeans++. We have addressed the loop closure detection part of the BoW-based SLAM methods in a self-supervised manner, by integrating an autoencoder for doing vector quantization. This approach can increase the accuracy of large-scale SLAM, where plenty of unlabeled data is available. The main advantage of using a self-supervised is that it can help reducing the amount of labeling. Furthermore, experiments show that autoencoders are far more efficient than semi-supervised methods like graph convolutional neural networks, in terms of speed and memory consumption. We integrated this method into the state of the art long range appearance based visual bag of word SLAM, FABMAP2, also in ORB-SLAM. Experiments demonstrate the superiority of this approach in indoor and outdoor datasets over regular FABMAP2 in all cases, and it achieves higher accuracy in loop closure detection and trajectory generation.
