| 基于卷积神经网络优化回环检测的视觉SLAM算法 |
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| 引用本文： | 郭烈,葛平淑,王肖,王东兴.基于卷积神经网络优化回环检测的视觉SLAM算法[J].西南交通大学学报,2021,56(4):706-712, 768. |
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| 作者姓名： | 郭烈 葛平淑 王肖 王东兴 |
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| 基金项目： | 国家自然科学基金（51975089，51575079）；中国博士后科学基金项目（2018M641688）；辽宁省教育厅科学研究经费项目（LJYT201915） |
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| 摘 要： | 传统视觉即时定位与建图（SLAM）算法若无回环检测可能会存在累积误差无法消除的现象，即使有回环检测，也因准确率和效率比较低而无法应用于轻量级设备上，为此，研究一种回环检测优化的视觉SLAM算法. 前端估计时，对相邻帧图像进行ORB （oriented fast and rotated brief）特征提取与匹配，对匹配成功的特征点进行PnP （perspective-n-point）求解，获得相机运动估计并筛选出关键帧图像；后端优化时，利用SqueezeNet卷积神经网络 （CNN）提取图像的特征向量，计算余弦相似度判断是否出现回环，若出现回环则在位姿图中增加相应约束，利用图优化理论对全局位姿进行整体优化；最后利用项目组制作的数据集和TUM （technical university of munich）公开数据集进行测试与对比. 研究结果表明:相比于无回环检测算法，本文方法可以成功检测到回环并为全局轨迹优化增添约束；相比于传统词袋法，在回环检测准确率相同的情况下，本文方法召回率可提高21%且计算耗时减少74%；与RGB-D （red green blue-depth） SLAM算法相比，本文方法建图误差可降低29%.
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| 关 键 词： | 视觉即时定位与建图 卷积神经网络 回环检测 图优化 |
| 收稿时间： | 2019-07-19 |
Visual Simultaneous Localization and Mapping Algorithm Based on Convolutional Neural Network to Optimize Loop Detection |
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| GUO Lie,GE Pingshu,WANG Xiao,WANG Dongxing.Visual Simultaneous Localization and Mapping Algorithm Based on Convolutional Neural Network to Optimize Loop Detection[J].Journal of Southwest Jiaotong University,2021,56(4):706-712, 768. |
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| Authors: | GUO Lie GE Pingshu WANG Xiao WANG Dongxing |
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| Abstract: | Traditional visual SLAM (simultaneous localization and mapping) without loop detection may lead to error accumulation. Even if there exits loop detection, it is unable to be applied to the lightweight applications because of its low accuracy and efficiency. Thus, a visual SLAM with loop detection optimization is studied. In the front-end estimation, ORB (oriented fast and rotated brief) feature points were abstracted and matched. PnP (perspective-n-point) was solved for the successful matched point to estimate the camera motion and screen out the key frame images. In the back-end optimization, SqueezeNet convolution neural network (CNN) was used to extract the feature vectors. The cosine similarities were calculated to determine whether there were loops or not. If there was a loop, the corresponding constraint was added to the posture graph. Then the global posture was optimized by using the graph optimization theory. Finally, tests and comparisons were conducted on the data sets produced by our research group and the public data sets of TUM. The results show that the proposed algorithm can detect loops successfully and add constraints to global trajectory optimization compared with the non-loop detection algorithm. Compared with the traditional word bag method, the recall rate of this method can be increased by 21% and the calculation time can be reduced by 74% under the same loop detection accuracy. Compared with RGB-D SLAM algorithm, the error of this method can be reduced by 29%. |
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