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基于 VGG16模型的快速闭环检测算法

VGG16 model-based fast loop closure detection algorithm

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摘要

深度卷积神经网络在图像特征表示方面优于传统手工特征, 将其用于闭环检测时还存在计算时间随着数据增长不断增加的问题。为了解决这一问题, 提出了一种基于 VGG16模型的快速闭环检测算法。该算法使用在 ImageNet上预训练的 VGG16网络模型提取图像卷积特征, 并通过一种自适应粒子滤波方法得到闭环候选帧, 以固定运算时间。在主流的闭环检测数据集 City Centre和 New College上对此算法进行测试, 实验结果显示, 该算法在两个数据集上可以分别达到 92%准确率下 70%召回率和 96%准确率下 61%召回率, 超过了同类算法, 并有效解决了计算时间增长的问题。

Abstract

Deep convolution neural network is superior to traditional manual features in the image feature representation, but it still has the problem that the calculation time increases with the increase of data in the loop-closure detection. In order to solve this problem, a fast closed-loop detection algorithm based on VGG16 model is proposed. The algorithm uses the VGG16 network model, which is pre-trained on ImageNet, to extract the image convolution features, and a loop-closure candidate frame is obtained by an adaptive particle filtering method to fix the operation time. The algorithm is tested in the mainstream loop-closure detection dataset City Centre and New College, and the experimental results show that the algorithm can achieve 70% recall rate under 92% accuracy and 61% recall rate under 96% accuracy on the two datasets, which exceeds the conventional algorithms, and effectively solves the problem of calculating time growth.

Newport宣传-MKS新实验室计划
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中图分类号:TP391.4

DOI:10.3969/j.issn.1005-5630.2019.03.004

所属栏目:应用技术

基金项目:国家科技重大仪器专项(2016YFF0101402)

收稿日期:2018-11-21

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作者单位    点击查看

张学典:上海理工大学光电信息与计算机工程学院, 上海 200093
顾璋琦:上海理工大学光电信息与计算机工程学院, 上海 200093
秦晓飞:上海理工大学光电信息与计算机工程学院, 上海 200093

联系人作者:张学典(zhangxuedian@hotmail.com)

备注:张学典 (1974—), 男, 教授, 研究方向为光电检测。

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引用该论文

ZHANG Xuedian,GU Zhangqi,QIN Xiaofei. VGG16 model-based fast loop closure detection algorithm[J]. Optical Instruments, 2019, 41(3): 20-26

张学典,顾璋琦,秦晓飞. 基于 VGG16模型的快速闭环检测算法[J]. 光学仪器, 2019, 41(3): 20-26

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