摘要
同时定位与构图(SLAM)主要用于解决移动机器人在未知环境中进行地图构建和导航的问题,是移动机器人实现自主移动的基础.闭环检测是视觉SLAM的关键步骤,对构建一致性地图和减少位姿累积误差具有重要作用.当前的闭环检测方法通常采用传统的SIFT、SURF等特征,很容易受到环境影响,为了提高闭环检测的准确性和鲁棒性,提出基于无监督栈式卷积自编码(CAEs)模型的特征提取方法,运用训练好的CAEs卷积神经网络对输入图像进行学习,将输出的特征应用于闭环检测.实验结果表明:与传统的BoW方法及其他基于深度学习模型的方法相比,所提出的算法能够有效降低图像特征的维数并改善特征描述的效果,可以在机器人SLAM闭环检测环节获得更好的精确性和鲁棒性.
As the foundation to realize the autonomous movement of mobile robots,simultaneous localization and mapping(SLAM),which is mainly used to solve the problem of mobile robots mapping and navigation in unknown environment,has been paid more attention in recent years.Loop closure detection,one of the key steps of visual SLAM,plays an important role to make a globally consistent map and reduce accumulated error of robot pose.Current methods for loop closure detection are vulnerable to environmental influence because they always adopt traditional features such as SIFT and SURF.To improve the accuracy and robustness of loop closure detection,a method based on unsupervised Stacked Convolutional Autoencoders(CAEs)model is proposed.The trained CAEs convolution neural network is used to learn from input images,while the output features are used for loop closure detection.The results of experiment show that the proposed method,compared with traditional BoW-based methods and other methods based on deep learning model,can effectively reduce the dimension of image features and improve the effect of feature description.Thus,it can attain better accuracy and robustness in loop closure detection of robot SLAM.
作者
张云洲
胡航
秦操
楚好
吴运幸
ZHANG Yun-zhou;HU Hang;QIN Cao;CHU Hao;WU Yun-xing(College of Information Science and Engineering,Northeastern University,Shenyang 110004,China;Faculty of Robot Science and Engineering,Northeastern University,Shenyang 110004,China)
出处
《控制与决策》
EI
CSCD
北大核心
2019年第5期981-988,共8页
Control and Decision
基金
国家自然科学基金项目(61471110
61733003)
国家重点研发计划项目(2017YFC080500015005)
中央高校基本科研业务费专项基金项目(N172608005
N160413002)
