Performance Evaluation of Deep Neural Networks in Detecting Loop Closure of Visual SLAM

被引：10

作者：

Chen, Yong ^{[1
]}

Zuo, Lin ^{[1
]}

Zhang, ChangHua ^{[1
]}

Liu, FengLian ^{[2
]}

Wu, YunFeng ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Chengdu, Peoples R China

[2] State Grid Sichuan Elect Power Res Inst, Chengdu, Peoples R China

来源：

2019 11TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC 2019), VOL 2 | 2019年

基金：

美国国家科学基金会;

关键词：

mobile robot; visual SLAM; loop closure detection; deep learning; REPRESENTATION;

D O I：

10.1109/IHMSC.2019.10136

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper concerns the problem of Loop Closure Detection (LCD) of visual Simultaneous Localization and Mapping (SLAM). The LCD is a crucial model to reduce the accumulative error in visual SLAM. The traditional LCD methods use hand-crafted features, which ignore useful information. We propose a LCD method based on Convolutional Neural Networks (CNNs) without any manual intervention for visual features. We compare and analyze several popular deep neural networks models for LCD. Two open datasets has been used to evaluate the performance of LCD in terms of mean-per-class accuracy. The results show that deep neural networks are feasible for LCD and the ResNet50 network outperforms the other deep neural networks.

引用

页码：171 / 175

页数：5