Visual Simultaneous Localization and Mapping (vSLAM) are expected to promote the initiatives in Smart City including driverless cars and intelligent robots. Loop closure detection (LCD) is an important module in a vSLAM system. Existing works with convolutional neural networks exhibit better performance on feature extraction, but this is far from enough. Concerning the characteristics of LCD, it is of great significance to have a customized loss function and a method to construct suitable training image sets. Based on this motivation, we propose a novel framework for LCD. Through a deep analysis of the distance relationships in the LCD problem, we propose the multi-tuplet clusters loss function together with mini-batch construction scheme. The proposed framework can map images to a low dimensional space and extract more discriminative image features, which help learn a more essential distance relationship of the LCD problem. Extensive evaluations demonstrate that our method outperforms many state-of-art approaches even in complex environments with strong appearance changes. Importantly, though the training process is computationally demanding, its online application is very efficient.

中文翻译：

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改进的深度学习，用于智能城市中的视觉环路闭合检测

可视化同时定位和地图绘制（vSLAM）有望推动包括无人驾驶汽车和智能机器人在内的智慧城市计划。闭环检测（LCD）是vSLAM系统中的重要模块。卷积神经网络的现有作品在特征提取方面表现出更好的性能，但这还远远不够。关于LCD的特性，具有定制的损失函数和构造合适的训练图像集的方法具有重要意义。基于这种动机，我们提出了一种新颖的LCD框架。通过对LCD问题中距离关系的深入分析，提出了多连峰簇的损失函数和小批量构造方案。所提出的框架可以将图像映射到低维空间，并提取出更具区别性的图像特征，这有助于了解LCD问题的更重要的距离关系。广泛的评估表明，即使在外观变化强烈的复杂环境中，我们的方法也优于许多最新方法。重要的是，尽管培训过程对计算要求很高，但其在线应用程序非常有效。