Abstract
The current Simultaneous Localization and Mapping (SLAM) systems are primarily based on the assumption of scene rigidity. However, the interference of dynamic objects in real-world environments and the computational limitations of small-scale computers pose significant challenges for the application of SLAM. To address these issues, this paper proposes a dynamic visual SLAM (VSLAM) system characterized by low computational cost, good stability, and maintained localization accuracy. This system first adopts an improved lightweight YOLOv8n algorithm for rapid detection of dynamic targets. Subsequently, a mask generation algorithm is proposed, which categorizes detection results into dynamic and static regions and eliminates feature points in the dynamic area using masks generated from bounding boxes, while preserving the features of static target areas. Finally, testing results on the TUM dataset demonstrate that compared to ORB-SLAM3, our system achieves an average reduction of 90.6% in the root mean square error of absolute trajectory error in highly dynamic sequences. Compared to DynaSLAM, our system significantly reduces the average tracking time while maintaining similar localization accuracy. Deployment tests on mobile platforms further confirm the system’s lower computational demands and robust operational performance.
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References
Ebadi, K., Bernreiter, L., Biggie, H.: Present and future of SLAM in extreme environments: the DARPA SubT challenge. IEEE Trans. Rob. 40, 936–959 (2023)
Engel, J., Sturm, J., Cremers, D.: Large-scale direct SLAM with stereo cameras. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 834–849. IEEE (2014)
Campos, C., Elvira, R., Rodríguez, J.J.G.: ORB-SLAM3: an accurate open-source library for visual, visual-inertial, and multimap slam. IEEE Trans. Robot. 37(6), 1874–1890 (2021)
Niculescu, V., Polonelli, T., Magno, M.: NanoSLAM: enabling fully onboard SLAM for tiny robots. IEEE Internet Things J. 11(8), 13584–13607 (2023)
Min, F., Wu, Z., Li, D.: COEB-SLAM: a robust VSLAM in dynamic environments combined object detection, epipolar geometry constraint, and blur filtering. IEEE Sens. J. 23(21), 26279–26291 (2023)
Wu, W., Guo, L., Gao, H.: YOLO-SLAM: a semantic SLAM system towards dynamic environment with geometric constraint. Neural Comput. Appl. 34(8), 6011–6026 (2022)
Cong, P., Li, J., Liu, J.: SEG-SLAM: dynamic indoor RGB-D visual SLAM integrating geometric and YOLOv5-based semantic information. Sensors 24(7), 2102 (2024)
Zhang, R., Zhang, X.: Geometric constraint-based and improved YOLOv5 semantic SLAM for dynamic scenes. ISPRS Int. J. Geo Inf. 12(6), 211 (2023)
Wu, W., Guo, L., Gao, H.: YOLO-SLAM: a semantic SLAM system towards dynamic environment with geometric constraint. Neural Comput. Appl., 1–16 (2022)
Kumar, A., Park, J., Behera, L.: High-speed stereo visual SLAM for low-powered computing devices. IEEE Robot. Autom. Lett. 9(1), 499–506 (2023)
Xie, W., Liu, P.X., Zheng, M.: Moving object segmentation and detection for robust RGBD-SLAM in dynamic environments. IEEE Trans. Instrum. Meas. 70, 1–8 (2020)
Yao, C., Ding, L., Lan, Y.: MOR-SLAM: a new visual SLAM system for indoor dynamic environments based on mask restoration. Mathematics 11(19), 4037 (2023)
Wang, X., Zhang, X.: MCBM-SLAM: an improved mask-region-convolutional neural network-based simultaneous localization and mapping system for dynamic environments. Electronics 12(17), 3596 (2023)
Yang, L., Cai, H.: Unsupervised video object segmentation for enhanced SLAM-based localization in dynamic construction environments. Autom. Constr. 158, 105235 (2024)
Martinez-Carranza, J., Hernández-Farías, D.I., Rojas-Perez, L.O.: Language meets YOLOv8 for metric monocular SLAM. J. Real-Time Image Proc. 20(4), 59 (2023)
Zhao, X., Song, Y.: Improved ship detection with YOLOv8 enhanced with MobileViT and GSConv. Electronics 12(22), 4666 (2023)
Acknowledgments
This research was funded by the National Natural Science Foundation of China (61973178), the Smart Grid Joint Fund of State Key Program of National Natural Science Foundation of China (U2066203), Major natural science projects of colleges and universities in Jiangsu Province (21KJA470006), and the Jiangsu Province Graduate Research and Practice Innovation Program Project (KYCX22 3347).
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Xue, Z., Xu, Y., Zhou, Y., Sun, F., Ding, Z., Lu, G. (2024). A Lightweight Visual Odometry Based on Object Detection for Dynamic Environment. In: Gu, J., Hu, F., Zhou, H., Fei, Z., Yang, E. (eds) Robotics and Autonomous Systems and Engineering Applications of Computational Intelligence. LSMS ICSEE 2024 2024. Communications in Computer and Information Science, vol 2220. Springer, Singapore. https://doi.org/10.1007/978-981-96-0313-8_10
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DOI: https://doi.org/10.1007/978-981-96-0313-8_10
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