Visual SLAM Loop Closure Detection Algorithm Based on Improved MobileNetV3

doi:10.3969/j.issn.1006-2475.2024.10.004

Abstract

Abstract: To address the inaccuracies in loop closure detection by traditional algorithms under variable lighting， with dynamic objects， and changing viewpoints， leading to the problem of large error in robot mapping， this paper introduces an algorithm using an enhanced MobileNetV3 for visual SLAM. The work improves the Coordinate Attention mechanism within the feature extraction network， enhancing spatial information extraction to meet loop detection needs. Features are then dimensionally reduced via an autoencoder and assessed for similarity to detect loop closures. Experimental results on the City Centre dataset indicate a 21.8 percentage points increase in detection accuracy and a significant speed improvement compared with traditional methods. This approach also more effectively reduces cumulative errors in visual SLAM systems， ensuring greater real-time performance.

Key words: , loop closure detection； deep learning； coordinating attention mechanism； autoencoder dimension reduction； similarity calculation

CLC Number:

TP391.4

YANG Jun1, HU Wei1, ZHU Wenfu2. Visual SLAM Loop Closure Detection Algorithm Based on Improved MobileNetV3[J]. Computer and Modernization, 2024, 0(10): 21-26.

References

［1］赵燕成，房桐，杜保帅，等. 移动机器人视觉SLAM回环检测现状研究［J］. 无线电工程， 2023，53（1）：129-139.
［2］李延真，石立国，徐志根，等. 移动机器人视觉SLAM研究综述［J］. 智能计算机与应用， 2022，12（7）：40-45.
［3］ CAMPOS C， ELVIRA R， RODRIGUEZ J J G， et al. ORB-SLAM3： An accurate open-source library for visual， visual-
inertial， and multimap SLAM［J］. IEEE Transactions on Robotics， 2021，37（6）：1874-1890.
［4］ MUR-ARTAL R， TARDOS J D. ORB-SLAM2： An open-source SLAM system for monocular， stereo， and RGB-D cameras［J］. IEEE Transactions on Robotics， 2017，33（5）：1255-1262.
［5］ GOMEZ-OJEDA R， MORENO F A， ZUNIGA-NOEL D， et al. PL-SLAM： A stereo SLAM system through the combination of points and line segments［J］. IEEE Transactions on Robotics， 2019，35（3）：734-746.
［6］ MEMON A R， WANG H S， HUSSAIN A. Loop closure detection using supervised and unsupervised deep neural networks for monocular SLAM systems［J］. Robotics and Autonomous Systems， 2020，126. DOI： 10.1016/j.robot.2020.103470.
［7］ ZHANG X W， WANG L， SU Y. Visual place recognition： A survey from deep learning perspective［J］. Pattern Recognition， 2021，113. DOI: 10.1016/j.patcog.2020.107760.
［8］陈超，齐峰. 卷积神经网络的发展及其在计算机视觉领域中的应用综述［J］. 计算机科学， 2019，46（3）：63-73.
［9］ LIU H， ZHAO C Y， HUANG W P， et al. An end-to-end siamese convolutional neural network for loop closure detection in visual SLAM system［C］// 2018 IEEE International Conference on Acoustics， Speech and Signal Processing （ICASSP）. IEEE， 2018：3121-3125.
［10］ TATENO K， TOMBARI F， LAINA I， et al. CNN-SLAM： Real-time dense monocular SLAM with learned depth prediction［C］// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. IEEE， 2017：6565-6574.
［11］李晓，马社祥，李啸. 基于卷积神经网络的回环检测算法［J］. 现代电子技术， 2022，45（1）：72-76.
［12］陈勇，党淑雯，聂铃.基于ResNet模型的回环检测算法［J］. 智能计算机与应用， 2022，12（8）：196-199.
［13］罗顺心，张孙杰. 基于卷积神经网络的回环检测算法［J］. 计算机与数字工程， 2019，47（5）：1020-1026.
［14］ HOWARD A， SANDLER M， CHEN B， et al. Searching for MobileNetV3［C］// Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. IEEE， 2019：1314-1324.
［15］来杰，王晓丹，向前，等. 自编码器及其应用综述［J］. 通信学报， 2021，42（9）：218-230.
［16］ BURGUERA A， BONIN-FONT F， GUERRERO E F， et al. Combining deep learning and robust estimation for outlier-resilient underwater visual graph SLAM［J］. Journal of Marine Science and Engineering， 2022，10（4）. DOI: 10.3390/JMSE10040511.
［17］杨雪梅，李帅永. 移动机器人视觉SLAM回环检测原理、现状及趋势［J］. 电子测量与仪器学报， 2022，36（8）：1-12.
［18］孔维刚，李文婧，王秋艳，等.基于改进YOLOv4算法的轻量化网络设计与实现［J］.计算机工程， 2022，48（3）：181-188.
［19］ ROY A G， NAVAB N， WACHINGER C. Recalibrating fully convolutional networks with spatial and channel “squeeze and excitation” blocks［J］. IEEE Transactions on Medical Imaging， 2018，38（2）：540-549.
［20］ WU J， DONG J M， NIE W Y， et al. A lightweight YOLOv5 optimization of coordinate attention［J］. Applied Sciences， 2023，13（3）. DOI: 10.3390/app13031746.
［21］唐东林，吴续龙，周立，等. 结合深度学习与注意力机制的墙体安全检测模型［J］. 安全与环境学报， 2022，22（1）：8-15.
［22］ ZHAO R， DU S H. An encoder-decoder with a residual network for fusing hyperspectral and panchromatic remote sensing images［J］. Remote Sensing， 2022，14（9）. DOI： 10.3390/rs14091981.
［23］郝天轩，陈国印，赵立桢，等. 基于Adam优化深度神经网络快速确定瓦斯抽采半径［J］. 中国安全生产科学技术，2022，18（6）：52-57.
［24］ MIRJALILI S M， TALEB H， KABIR M Z， et al. Design optimization of orbital angular momentum fibers using the gray wolf optimizer［J］. Applied Optics， 2020，59（20）：6181-6190.
［25］ BABU D V， KARTHIKEYAN C， Shreya， et al. Performance analysis of cost and accuracy for whale swarm and RMSprop optimizer［C］// 2020 IOP Conference Series： Materials Science and Engineering. IOP Publishing， 2020. DOI： 10.1088/1757-889X/993/1/012080.
［26］ FILZASAVITRA P， PURBOYO T W， SAPUTRA R E. Analysis of steganography on PNG image using least significant bit（LSB）， peak signal to noise ratio（PSNR） and mean square error （MSE）［J］. Journal of Engineering and Applied Sciences， 2019，14（21）：7821-7827.
［27］ BALSAS C J L. City center revitalization in Portugal： Lessons from two medium size cities［J］. Cities， 2000，17（1）：19-31.
［28］ SMITH M， BALDWIN I， CHURCHILL W， et al. The new college vision and laser data set［J］. The International Journal of Robotics Research， 2009， 28（5）：595-599.

[1]	ZHENG Jiuchao, ZHAO Xinyuan. Entity Linking Method Based on Topics and Description Information [J]. Computer and Modernization, 2024, 0(12): 10-14.
[2]	ZHAO Chenyang, XUE Tao, LIU Junhua. Fashion Clothing Pattern Generation Based on Improved Stable Diffusion [J]. Computer and Modernization, 2024, 0(12): 15-23.
[3]	WANG Xiaohang1, LI Yongjie1, YU Lei1, FAN Xiao2. A Method of Using Compound Event Probability Operation to Solve Problem of Negative Information Blocking Maximization [J]. Computer and Modernization, 2024, 0(12): 24-33.
[4]	ZHANG Xiaodong1, BAI Guangzhi1, LI Min1, LI Haoyang2. Oil and Gas Well Production Prediction Model Based on Empirical Wavelet Transform [J]. Computer and Modernization, 2024, 0(12): 53-58.
[5]	LIU Yunhai1, Feng Guang1, WU Xiaoting2, YANG Qun2 . Safety Helmet Wearing Detection Algorithm for Complex Construction Scenes [J]. Computer and Modernization, 2024, 0(12): 66-71.
[6]	LIU Baobao, YANG Jingjing, TAO Lu, WANG Heying . DSMSC Based on Attention Mechanism for Remote Sensing Image Scene Classification [J]. Computer and Modernization, 2024, 0(12): 72-77.
[7]	GU Yue, DENG Songfeng, SHEN Ji, MU Wentao, ZHAO Enqi. SAR Ship Detection Algorithm Based on Improved YOLOv8 [J]. Computer and Modernization, 2024, 0(12): 78-83.
[8]	WU Xiuling1, ZHOU Sheng1, WANG Chunjuan1, YU Cuizhuo2, LIU Hao3. Research Progress in Ultra Short-term Power Load Forecasting Technology [J]. Computer and Modernization, 2024, 0(12): 108-115.
[9]	LI Deyou1, 2, YU Jinsongdi1, 2, WEI Dandan1, 2, LUO Yuan1, 2, TONG Ruiju3. Abstract Tree Model for Gridded Cube Metadata [J]. Computer and Modernization, 2024, 0(11): 1-6.
[10]	GONG Yicheng1, 2, LIU Qing1, 2. Beijing Opera Binary Classification Based on RF-LCE-BiLSTM-Attention-AMSSA Model [J]. Computer and Modernization, 2024, 0(11): 7-12.
[11]	LI Taoying, LI Meng, WU Mengqiao. Taxi Passenger Flow Prediction Based on Heterogeneous Spatiotemporal Graph#br# Convolutional Networks [J]. Computer and Modernization, 2024, 0(11): 13-18.
[12]	ZHANG Tai1, YAN Zihao2, DUAN Jie2, ZHANG Zhihong2. Information Forwarding Strategy of Internet of Vehicles in Named Data Network [J]. Computer and Modernization, 2024, 0(11): 19-27.
[13]	YUAN Qingle, MU Li. Inventory Forecasting Method Based on Improved Elman Neural Network [J]. Computer and Modernization, 2024, 0(11): 28-33.
[14]	ZHANG Kun1, ZHANG Yongwei1, WU Yongcheng1, ZHANG Xiaowen2, ZHAI Shichen2. An LLM-based Method for Automatic Construction of Equipment Failure Knowledge Graphs [J]. Computer and Modernization, 2024, 0(11): 46-53.
[15]	YE Xue, YANG Sheng, CHENG Kai, ZHU Feng. A Financial Knowledge Q&A Model for Power Enterprise Based on ChatGLM2-6B [J]. Computer and Modernization, 2024, 0(11): 54-63.