Analysis of feature point matching technology in SLAM based on binocular vision
DOI:
https://doi.org/10.54097/hset.v52i.8723Keywords:
Computer vision; SLAM; Feature point matching algorithm.Abstract
With rapidly developing computing technology, intelligent robots have been used extensively in many areas of life, improving work efficiency and lowering labor costs. At the same time, with the increasing number of robot application scenarios, the demand for robot intelligence has also increased. Computer vision-based simultaneous localization and mapping (SLAM) technology is a key technology that helps robots achieve real-time positioning and navigation in order to improve their intelligence. Feature point matching is an important component of this technology. This paper mainly analyzes the current development status of feature point matching technology in visual SLAM. Firstly, a brief introduction was given to the three widely used SLAMs, and the advantages and disadvantages of different SLAM technologies were analyzed. Second, a brief presentation and explanation of the working principle of binocular SLAM technology were provided. Then, the basic principles and key points of feature point recognition and matching techniques in several different algorithms for visual SLAM were summarized. Analyzed the advantages and disadvantages of various algorithms, as well as improvements based on this algorithm. Finally, summarize and provide suggestions for the future development of visual SLAM technology.
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XiePing Gong, LiZhong Song, and Yang Yin "Review of visual SLAM", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022),
Liu, Chang, and Shuwen Dang. "The Research and Application of Improved ORB Feature Matching Algorithm." Advances in Guidance, Navigation and Control: Proceedings of 2022 International Conference on Guidance, Navigation and Control. Singapore: Springer Nature Singapore, 2023.
Y. Xin and W. Li-Na, "Review of Research on Robot Visual Slam," 2022 34th Chinese Control and Decision Conference (CCDC), Hefei, China, 2022, pp. 6080-6085, doi: 10.1109/CCDC55256.2022.10034316.
Wei, YueQiang, et al. "A Fast 3D Laser SLAM Method for Indoor Scenarios." 2022 34th Chinese Control and Decision Conference (CCDC). IEEE, 2022.
Gökcen, Berkay, and Erkan Uslu. "Object Aware RGBD SLAM in Dynamic Environments." 2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). IEEE, 2022.
Fu, Xixi, et al. "Recognition and Location Based on Fusion of FAST Algorithm and SIFT Algorithm." 2021 IEEE 21st International Conference on Communication Technology (ICCT). IEEE, 2021.
Ruan, Tianshu, V. Amrusha Aryasomyajula, and Nasser Houshangi. "Performance of monocular and stereo camera in indoor environment for Visual SLAM using ORB method." 2022 IEEE International Conference on Electro Information Technology (eIT). IEEE, 2022.
Yuan, Jing, et al. "ORB-TEDM: An RGB-D SLAM approach fusing ORB triangulation estimates and depth measurements." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1-15.
Liu, Jia, et al. "VSLAM method based on object detection in dynamic environments." Frontiers in Neurorobotics (2022).
Ni, Jianjun, et al. "An improved adaptive ORB-SLAM method for monocular vision robot under dynamic environments." International Journal of Machine Learning and Cybernetics 13.12 (2022): 3821-3836.
Jiang, Rui, et al. "Road-constrained geometric pose estimation for ground vehicles." IEEE Transactions on Automation Science and Engineering 17.2 (2019): 748-760.
Fu, Xixi, et al. "Recognition and Location Based on Fusion of FAST Algorithm and SIFT Algorithm." 2021 IEEE 21st International Conference on Communication Technology (ICCT). IEEE, 2021.
Alcantarilla, Pablo F., and T. Solutions. "Fast explicit diffusion for accelerated features in nonlinear scale spaces." IEEE Trans. Patt. Anal. Mach. Intell 34.7 (2011): 1281-1298.
Srividhya, S., S. Prakash, and K. Elangovan. "3D reconstruction of an indoor environment using SLAM with modified SURF and A-KAZE feature extraction algorithm." Intelligent Computing, Information and Control Systems: ICICCS 2019. Springer International Publishing, 2020.
Zhang, Tong, et al. "A new visual inertial simultaneous localization and mapping (SLAM) algorithm based on point and line features." Drones 6.1 (2022): 23.
Muja, Marius, and David G. Lowe. "Fast approximate nearest neighbors with automatic algorithm configuration." VISAPP (1) 2.331-340 (2009): 2.
He,Zhiyuan, “21 projects play deep learning” Electronic Industry Press (2018): 88-90
Zhang, Feng, et al. "Research of Visual SLAM in Dynamic Environment using Convolutional Neural Network." 2022 International Conference on 3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME). IEEE, 2022.
Yunfeng, GAO, Zhou Lun, and Lv Mingrui. "A review of indoor positioning methods for autonomous mobile robots." Transducer and Microsystem 32.12 (2013): 1-5.
Steenbeek, Anne, and Francesco Nex. "CNN-based dense monocular visual SLAM for real-time UAV exploration in emergency conditions." Drones 6.3 (2022): 79.
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