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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.