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

2019 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

无线电电子学;计算机科学;材料科学

Wang, Fei^1^2 ; Ruan, Xiaogang^1^2 ; Huang, Jing^1^2

Faculty of Information Technology, Beijing University of Technology, Beijing

100124, China^1

Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing

100124, China^2

关键词: Accumulation errors;    Autonomous movement;    Dimensionality reduction;    Dynamic environments;    Environmental change;    Image transformations;    Simultaneous localization and mapping;    Visual simultaneous localization and mappings;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/563/5/052082/pdf
DOI  :  10.1088/1757-899X/563/5/052082

来源: IOP

【 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.