Open Access

ARTICLE

PPS-SLAM: Dynamic Visual SLAM with a Precise Pruning Strategy

Jiansheng Peng^1,2,3,4,*, Wei Qian¹, Hongyu Zhang¹

1 College of Automation, Guangxi University of Science and Technology, Liuzhou, 545000, China
2 Department of Artificial Intelligence and Manufacturing, Hechi University, Hechi, 546300, China
3 Key Laboratory of AI and Information Processing, Education Department of Guangxi Zhuang Autonomous Region, Hechi, 546300, China
4 Guangxi Key Laboratory of Sericulture Ecology and Applied Intelligent Technology, School of Chemistry and Bioengineering, Hechi University, Hechi, 546300, China

* Corresponding Author: Jiansheng Peng. Email:

Computers, Materials & Continua 2025, 82(2), 2849-2868. https://doi.org/10.32604/cmc.2024.058028

Received 02 September 2024; Accepted 28 October 2024; Issue published 17 February 2025

Abstract

Dynamic visual SLAM (Simultaneous Localization and Mapping) is an important research area, but existing methods struggle to balance real-time performance and accuracy in removing dynamic feature points, especially when semantic information is missing. This paper presents a novel dynamic SLAM system that uses optical flow tracking and epipolar geometry to identify dynamic feature points and applies a regional dynamic probability method to improve removal accuracy. We developed two innovative algorithms for precise pruning of dynamic regions: first, using optical flow and epipolar geometry to identify and prune dynamic areas while preserving static regions on stationary dynamic objects to optimize tracking performance; second, propagating dynamic probabilities across frames to mitigate the impact of semantic information loss in some frames. Experiments show that our system significantly reduces trajectory and pose errors in dynamic scenes, achieving dynamic feature point removal accuracy close to that of semantic segmentation methods, while maintaining high real-time performance. Our system performs exceptionally well in highly dynamic environments, especially where complex dynamic objects are present, demonstrating its advantage in handling dynamic scenarios. The experiments also show that while traditional methods may fail in tracking when semantic information is lost, our approach effectively reduces the misidentification of dynamic regions caused by such loss, thus improving system robustness and accuracy.

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Keywords

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