文件名称：高翔的无监督回环检测方法
文件大小：3.24MB
文件格式：PDF
更新时间：2021-09-23 10:18:39
SLAM Loop Closure
This paper is concerned of the loop closure detection problem for visual simultaneous localization and mapping systems.We propose a novel approach based on the stacked denoising auto-encoder (SDA), a multi-layer neural network that autonomously learns an compressed representation from the rawinput data in an unsupervisedway.Different with the traditional bag-of-words based methods, the deep network has the ability to learn the complex inner structures in image data, while no longer needs to manually design the visual features. Our approach employs the characteristics of the SDA to solve the loop detection problem. The workflow of training the network, utilizing the features and computing the similarity score is presented. The performance of SDA is evaluated by a comparison study with Fab-map 2.0 using data from open datasets and physical robots. The results show that SDA is feasible for detecting loops at a satisfactory precision and can therefore provide an alternative way for visual SLAM systems.