计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 228-232.doi: 10.11896/jsjkx.190900034
汪丹1, 石朝侠1, 王燕清2
WANG Dan1, SHI Chao-xia1, WANG Yan-qing2
摘要： 闭环检测是同时定位与建图(Simultaneous localization and mapping,SLAM)的重要组成部分,能够有效减小SLAM系统中的累积误差,并且如果在定位与建图过程中跟踪丢失,还可以利用闭环检测进行重定位。与传统的手动设计的特征(hand-crafted feature)相比,从神经网络中学习到的图像特征具有更好的环境不变性和语义识别能力。考虑到基于陆标(landmark)的卷积特征能够克服整个图像特征对视点变化敏感的缺陷,文中提出了一种新的闭环检测算法。其首先通过卷积神经网络的卷积层直接识别出图像的感兴趣区域生成陆标,然后对图像中识别出的每个陆标提取卷积特征,生成图像的最终表示以检测闭环。为了验证算法的有效性,在典型的数据集上进行了对比实验,结果表明所提算法具有优异的性能,且即使是在极端的视点和外观变化的情况下仍然具有高鲁棒性。
中图分类号:
| [1]VAKHITOV A,LEMPITSKY V.Learnable Line Segment Descriptor for Visual SLAM[J].IEEE Access,2019(7):39923-39934. [2]GAO X,ZHANG T,LIU Y,et al.Visual SLAM XIV:From Theory to Practice[M].Beijing:Electronic Industry Press,2017. [3]LOWRY,STEPHANIE M,et al.Visual Place Recognition:A Survey[J].IEEE Transactions on Robotics,2016,32 (1):1-19. [4]ROTTMANN N,BRUDER R,SCHWEIKARD A,et al.Loop Closure Detection in Closed Environments[J].arXiv:1908.04558. [5]CHEN J,LI J,XUY,et al.A compact loop closure detection based on spatialpartitioning[C]//International Conference on Image.IEEE,2017. [6]MILFORD M J,WYETH G F.SeqSLAM:Visual route-based navigation for sunny summer days and stormy winter nights[C]//2012 IEEE International Conference on Robotics and Automation.IEEE,2012. [7]BAMPIS L,AMANATIADIS A,GASTERATOS A.Fast loop-closure detection using visual-word-vectors from image sequences[J].The International Journal of Robotics Research,2017,37(1):62-82. [8]MUR-ARTALR,TARDOSJ D.ORB-SLAM2:An Open-SourceSLAM System for Monocular,Stereo,and RGB-D Cameras[J].IEEE Transactions on Robotics,2017,33(5):1255-1262. [9]CUMMINS M,NEWMANP.FAB-MAP:Probabilistic Localization and Mapping in the Space of Appearance[J].The International Journal of Robotics Research,2008,27(6):647-665. [10]LOWED G.Object Recognition from Local Scale-Invariant Features[C]//Computer Vision,1999.The Proceedings of the Seventh IEEE International Conference on.IEEE,1999. [11]XU A,NAMITG.SURF:Speeded-Up Robust Features[J].Computer Vision & Image Understanding,2008,110(3):404-417. [12]OLIVA,AUDE,TORRALBA,et al.Building the gist of ascene:the role of global image features in recognition[J].Progress in brain research,2006,155:23-36. [13]DALAL N,TRIGGS B.Histograms of Oriented Gradients for Human Detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05).IEEE,2005. [14]CHEN Z,LAM O,JACOBSON A,et al.Convolutional Neural Network-based Place Recognition[C]//Proceedings of the 16th Australasian Conference on Robotics and Automation.2014. [15]SERMANET P,EIGEN D,ZHANG X.Overfeat:Integratedrecognition,localization and detection using convolutional networks[J].arXiv:1312.6229. [16]BAI D,WANG C,ZHANG B,et al.Sequence Searching with CNN Features for Robust and Fast Visual Place Recognition[J].Computers & Graphics,2018,70:270-280. [17]GOMEZ-OJEDA R,LOPEZ-ANTEQUERA M,PETKOV N,et al.Training a Convolutional Neural Network for Appearance-Invariant Place Recognition[J].arXiv:1505.07428. [18]CHEN Z,JACOBSON A,SUNDERHUF N,et al.Deep learning features at scale for visual place recognition[C]//IEEE International Conference on Robotics and Automation (ICRA),2017:3223-3230. [19]MERRILL N,HUANGG.Lightweight Unsupervised Deep Loop Closure[J].arXiv:1805.07703v01. [20]SÜNDERHAUF,NIKO,SHIRAZI S,Jacobson A,et al.Place recognition with ConvNet landmarks:Viewpoint-robust,condition-robust,training-free[M]//Proceedings of the 2010 Academy of Marketing Science (AMS) Annual Conference.Springer International Publishing,2015. [21]HOU Y,ZHANG H,ZHOU S.BoCNF:efficient image matc-hing with Bag of ConvNet features for scalable and robust visual place recognition[J].Autonomous Robots,2017,42(9):1-17. [22]MATTHEW D Z,FERGUS R.Visualizing and understanding convolutional networks[J].arXiv:1311.2901. [23]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014. [24]HE K,ZHANG X,REN S,et al.Deep residual learning for im-age recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778. [25]SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:1-9. [26]KRIZHEVSKY A,SUTSKEVER I,HINTON G.ImageNetClassification with Deep Convolutional Neural Networks[C]//NIPS.Curran Associates Inc.2012. [27]SÜNDERHAUF N,DAYOUB F,SHIRAZI S,et al.On the Performance of ConvNet Features for Place Recognition[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).2015:4297-4304. [28]DASGUPTA S.Experiments with Random Projection[C]//Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence.2013:143-151. [29]BRIGHAM E,MANNILA H.Random projection in dimensionality reduction:Applications to image and text data[C]//Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2001:245-250. [30]ZHOU B,LAPEDRIZA A,KHOSLA A,et al.Places:A 10 million Image Database for Scene Recognition[J].IEEE Transactions on Pattern Analysis and machine Intelligence,2017,pp(99):1-1. |
| [1] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
| [2] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
| [3] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
| [4] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
| [5] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
| [6] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
| [7] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
| [8] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
| [9] | 程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157 |
| [10] | 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018 |
| [11] | 周慧, 施皓晨, 屠要峰, 黄圣君. 基于主动采样的深度鲁棒神经网络学习 Robust Deep Neural Network Learning Based on Active Sampling 计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044 |
| [12] | 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫. 小样本雷达辐射源识别的深度学习方法综述 Survey of Deep Learning for Radar Emitter Identification Based on Small Sample 计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138 |
| [13] | 王君锋, 刘凡, 杨赛, 吕坦悦, 陈峙宇, 许峰. 基于多源迁移学习的大坝裂缝检测 Dam Crack Detection Based on Multi-source Transfer Learning 计算机科学, 2022, 49(6A): 319-324. https://doi.org/10.11896/jsjkx.210500124 |
| [14] | 楚玉春, 龚航, 王学芳, 刘培顺. 基于YOLOv4的目标检测知识蒸馏算法研究 Study on Knowledge Distillation of Target Detection Algorithm Based on YOLOv4 计算机科学, 2022, 49(6A): 337-344. https://doi.org/10.11896/jsjkx.210600204 |
| [15] | 刘伟业, 鲁慧民, 李玉鹏, 马宁. 指静脉识别技术研究综述 Survey on Finger Vein Recognition Research 计算机科学, 2022, 49(6A): 1-11. https://doi.org/10.11896/jsjkx.210400056 |
|
||
首页