Visual Loop Closure Detection Based on Stacked Convolutional and Autoencoder Neural Networks

作     者：Fei Wang Xiaogang Ruan Jing Huang 

作者机构：Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China 

出 版 物：《IOP Conference Series: Materials Science and Engineering》 

年 卷 期：2019年第563卷第5期

摘      要：Simultaneous localization and mapping is the basis for solving the problem of robotic autonomous movement. Loop closure detection is vital for visual simultaneous localization and mapping. Correct detection of closed loops can effectively reduce the accumulation error of the robot poses, which plays an important role in building a globally consistent environment map. Traditional loop closure detection adopts the method of extracting handcrafted image features, which are sensitive to dynamic environments and are poor in robustness. In this paper, a method called stacked convolutional and autoencoder neural networks is proposed to automatically extract image features and perform dimensionality reduction processing. These features have multiple invariances in image transformation. Therefore, this method is robust to environmental changes. Experiments on public datasets show that the proposed method is superior to traditional methods in terms of accuracy, recall, and average accuracy, thereby validating the effectiveness of the proposed method.