Application of SLAM Algorithm in The Construction of 2-D Maps

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Zhongwei, Liu
Jinyan Li
Wei Wu
Mingtao Pan
Kao Liu
Hang Yang


 In today's society, maps, as an important tool for human spatial cognition and spatial thinking, solidify and abstract the results of spatial cognition to complete the visual expression and information transmission of geographic information, and provide auxiliary decision-making for people to understand the urban pattern, formulate travel routes, take a taxi or self-drive navigation and other geospatial activities. The role of this is obvious, human beings have more strict requirements for maps, and the algorithms that can draw maps are also blooming. The purpose of this paper is to study the application of SLAM algorithm in the construction of simple 2-D maps, analyze the advantages of SLAM algorithm in the construction of 2-D maps, and analyze the role of closed-loop detection and trajectory optimization in the process of building 2-D maps. SLAM includes lidar data mapping, which has high measurement accuracy and stable measurement performance, and is now more widely used in industry.

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Liu, Z., Li, J., Wu, W., Pan, M., Liu, K., & Yang, H. (2024). Application of SLAM Algorithm in The Construction of 2-D Maps. Journal of Research in Multidisciplinary Methods and Applications, 3(6), 01240306001. Retrieved from


Mur-Artal R, Tardós 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

Campos C, Elvira R, Rodríguez J J G, et al. ORB-SLAM3: An accurate open-source library for visual, visual–inertial, and multimapslam[J]. IEEE Transactions on Robotics, 2021, 37(6): 1874-1890.

Engel J, Schöps T, Cremers D. LSD-SLAM: Large-scale direct monocular SLAM[C]//European conference on computer vision. Springer, Cham, 2014: 834-849.

Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381-395.

Sun Y, Liu M, Meng M Q H. Improving RGB-D SLAM in dynamic environments: A motion removal approach[J]. Robotics and Autonomous Systems, 2017, 89: 110-122.

Zou Bin, Lin Siyang, Yin Zhishuai. Semantic map construction based on YOLOv3 and visual SLAM[J].Advances in Laser and Optoelectronics,2020,57(20): 201012. Zou B, Lin S Y, Yin Z S. Semantic maping based on YOLOv3 and visual SLAM [J]. Laser& Optoelectronics Progress, 2020, 57 ( 20 ):201012.

Bescos B, Fácil J M, Civera J, et al. DynaSLAM: Tracking, mapping, and inpainting in dynamic scenes[J]. IEEE Robotics and Automation Letters, 2018, 3(4): 4076-4083.

Wang Mengyao , Song Wei . RGB-D SLAM Algorithm Based on Self-Adaptive Semantic Segmentation in Dynamic Scenarios[J]. Robot,2023,45(1):16-27. Wang M Y, S W. An RGB-D SLAM algorithm based on adaptive semantic segmentation in dynamic scenes [J]. Robot, 2023, 45( 1): 16-27.

Li S, Lee D. RGB-D SLAM in dynamic environments using static point weighting[J]. IEEE Robotics and Automation Letters, 2017, 2(4): 2263-2270.

Li A, Wang J, Xu M, et al. DP-SLAM: A visual SLAM with moving probability towards dynamic environments[J]. Information Sciences, 2021, 556: 128-142.

He K, Gkioxari G, Dollár P, et al. Mask r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2961-2969.

Long J, Shelhamer E, Darrell T. Fully convolutional networks for semanticsegmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 3431-3440.

Hu Xiangyan. Research on closed-loop detection method of SLAM based on particle swarm optimization[D]. Xi'an: Xidian University, 2018(in Chinese) (HU Xiangyan. Research on SLAM closed-loop detection method based on particle swarm optimization algorithm[D]. Xi'an: Xidian University, 2018)

Wei Shuangfeng, Pang Fan, Liu Zhenbin, et al. Survey of LiDAR-based SLAM algorithm[J]. Application Research of Computers, 2020, 37(2): 327-332(in Chinese) (WEI Shuangfeng, PANG Fan, LIU Zhenbin, et al.) Review of simultaneous localization and map construction methods based on LiDAR[J]. Application Research of Computers, 2020, 37(2): 327-332)

Steder B, Ruhnke M, Grzonka S, et al. Place recognition in 3D scans using a combination of bag of words and point feature based relative pose estimation[C] //Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Los Alamitos: IEEE Computer Society Press, 2011: 1249-1255

Li R H, Wang S, Gu D B. DeepSLAM: a robust monocular SLAM system with unsupervised deep learning[J]. IEEE Transactions on Industrial Electronics, 2021, 68(4): 3577-3587

Bescos B, Fácil J M, Civera J, et al. DynaSLAM: tracking, mapping, and inpainting in dynamic scenes[J]. IEEE Robotics and Automation Letters, 2018, 3(4): 4076-4083

Han X F, Laga H, Bennamoun M. Image-based 3D object reconstruction: state-of-the-art and trends in the deep learning era[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(5): 1578-1604

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