Title: DEP-SLAM: a dynamic environment perception SLAM system with large language models

Authors: Ying He; F. Richard Yu; Guang Zhou

Addresses: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China ' College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China; School of Information Technology, Carleton University, Ottawa, ON, Canada ' DeepRoute AI Inc., No. C701, Building 6, Shenjiu Science and Technology Innovation Park, Fubao Community, Fubao Street, Futian District, Shenzhen City, China

Abstract: Simultaneous localisation and mapping (SLAM) is crucial for robot navigation and building environment maps in real time. Most visual SLAM (VSLAM) systems assume that objects in the environment are static. However, in highly dynamic environments where this assumption fails, the system's efficiency can be seriously affected. Furthermore, current VSLAM systems are unable to interact with the environment or adapt their operating strategies to environmental changes. In this paper, we introduce DEP-SLAM, a dynamic environment perception SLAM system with large language models. The lightweight object detection network YOLOV7-Tiny is used to obtain semantic information. Large language models are used to dynamically perceive changes in the environment. To minimise manual hyperparameter tuning, we propose an enhanced geometric constraint method to better filter out and eliminate dynamic feature points. Experimental results demonstrate the superiority of DEP-SLAM over ORB-SLAM2, especially in terms of accuracy and robustness in highly dynamic indoor environments.

Keywords: visual SLAM; VSLAM; large language models; LLMs; dynamic environment; object detection.

DOI: 10.1504/IJSNET.2026.153119

International Journal of Sensor Networks, 2026 Vol.50 No.4, pp.247 - 259

Received: 20 Oct 2024
Accepted: 02 Nov 2024
Published online: 22 Apr 2026 *

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