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Open Access

ARTICLE

DKP-SLAM: A Visual SLAM for Dynamic Indoor Scenes Based on Object Detection and Region Probability

Menglin Yin1, Yong Qin1,2,3,4,*, Jiansheng Peng1,2,3,4
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: Yong Qin. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.057460

Received 18 August 2024; Accepted 23 October 2024; Published online 22 November 2024

Abstract

In dynamic scenarios, visual simultaneous localization and mapping (SLAM) algorithms often incorrectly incorporate dynamic points during camera pose computation, leading to reduced accuracy and robustness. This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability. Firstly, a parallel thread employs the YOLOX object detection model to gather 2D semantic information and compensate for missed detections. Next, an improved K-means++ clustering algorithm clusters bounding box regions, adaptively determining the threshold for extracting dynamic object contours as dynamic points change. This process divides the image into low dynamic, suspicious dynamic, and high dynamic regions. In the tracking thread, the dynamic point removal module assigns dynamic probability weights to the feature points in these regions. Combined with geometric methods, it detects and removes the dynamic points. The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms, providing better pose estimation accuracy and robustness in dynamic environments.

Keywords

Visual SLAM; dynamic scene; YOLOX; K-means++ clustering; dynamic probability

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