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Volume 15
Issue 1
10.3390/ijgi15010007
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

TAS-SLAM: A Visual SLAM System for Complex Dynamic Environments Integrating Instance-Level Motion Classification and Temporally Adaptive Super-Pixel Segmentation

by
Yiming Li
1,
Liuwei Lu
1,*,
Guangming Guo
2,
Luying Na
1,
Xianpu Liang
1,
Peng Su
1,
Qi An
3 and
Pengjiang Wang
4
1
Mechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192, China
2
Intelligent Equipment Research Institute, Beijing Academy of Science and Technology, Beijing 100061, China
3
State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
4
China Academy of Safety Science and Technology, Beijing 100012, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2026, 15(1), 7; https://doi.org/10.3390/ijgi15010007 (registering DOI)
Submission received: 13 November 2025 / Revised: 12 December 2025 / Accepted: 18 December 2025 / Published: 21 December 2025
(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)
Versions Notes

Abstract

To address the issue of decreased localization accuracy and robustness in existing visual SLAM systems caused by imprecise identification of dynamic regions in complex dynamic scenes—leading to dynamic interference or reduction in valid static feature points, this paper proposes a dynamic visual SLAM method integrating instance-level motion classification, temporally adaptive super-pixel segmentation, and optical flow propagation. The system first employs an instance-level motion classifier combining residual flow estimation and a YOLOv8-seg instance segmentation model to distinguish moving objects. Then, temporally adaptive super-pixel segmentation algorithm SLIC (TA-SLIC) is applied to achieve fine-grained dynamic region partitioning. Subsequently, a proposed dynamic region missed-detection correction mechanism based on optical flow propagation (OFP) is used to refine the missed-detection mask, enabling accurate identification and capture of motion regions containing non-rigid local object movements, undefined moving objects, and low-dynamic objects. Finally, dynamic feature points are removed, and valid static features are utilized for pose estimation. The localization accuracy of the visual SLAM system is validated using two widely adopted datasets, TUM and BONN. Experimental results demonstrate that the proposed method effectively suppresses interference from dynamic objects (particularly non-rigid local motions) and significantly enhances both localization accuracy and system robustness in dynamic environments.
Keywords: complex dynamic environments; visual SLAM; instance-level motion classification; temporally adaptive segmentation; optical flow propagation; localization accuracy complex dynamic environments; visual SLAM; instance-level motion classification; temporally adaptive segmentation; optical flow propagation; localization accuracy

Share and Cite

MDPI and ACS Style

Li, Y.; Lu, L.; Guo, G.; Na, L.; Liang, X.; Su, P.; An, Q.; Wang, P. TAS-SLAM: A Visual SLAM System for Complex Dynamic Environments Integrating Instance-Level Motion Classification and Temporally Adaptive Super-Pixel Segmentation. ISPRS Int. J. Geo-Inf. 2026, 15, 7. https://doi.org/10.3390/ijgi15010007

AMA Style

Li Y, Lu L, Guo G, Na L, Liang X, Su P, An Q, Wang P. TAS-SLAM: A Visual SLAM System for Complex Dynamic Environments Integrating Instance-Level Motion Classification and Temporally Adaptive Super-Pixel Segmentation. ISPRS International Journal of Geo-Information. 2026; 15(1):7. https://doi.org/10.3390/ijgi15010007

Chicago/Turabian Style

Li, Yiming, Liuwei Lu, Guangming Guo, Luying Na, Xianpu Liang, Peng Su, Qi An, and Pengjiang Wang. 2026. "TAS-SLAM: A Visual SLAM System for Complex Dynamic Environments Integrating Instance-Level Motion Classification and Temporally Adaptive Super-Pixel Segmentation" ISPRS International Journal of Geo-Information 15, no. 1: 7. https://doi.org/10.3390/ijgi15010007

APA Style

Li, Y., Lu, L., Guo, G., Na, L., Liang, X., Su, P., An, Q., & Wang, P. (2026). TAS-SLAM: A Visual SLAM System for Complex Dynamic Environments Integrating Instance-Level Motion Classification and Temporally Adaptive Super-Pixel Segmentation. ISPRS International Journal of Geo-Information, 15(1), 7. https://doi.org/10.3390/ijgi15010007

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