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Open Access

ARTICLE

Design of a Patrol and Security Robot with Semantic Mapping and Obstacle Avoidance System Using RGB-D Camera and LiDAR

Shu-Yin Chiang*, Shin-En Huang
Department of Electrical Engineering, Ming Chuan University, No. 5 De Ming Rd., Gui Shan District, Taoyuan, 333, Taiwan
* Corresponding Author: Shu-Yin Chiang. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.074528

Received 13 October 2025; Accepted 10 December 2025; Published online 29 December 2025

Abstract

This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping (SLAM), real-time object recognition, and dynamic obstacle avoidance. The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping, fusing geometric and visual data to build a high-fidelity 2D semantic map. This map enables the robot to identify and project object information for improved situational awareness. Experimental results show that object recognition reached 95.4% mAP@0.5. Semantic completeness increased from 68.7% (single view) to 94.1% (multi-view) with an average position error of 3.1 cm. During navigation, the robot achieved 98.0% reliability, avoided moving obstacles in 90.0% of encounters, and replanned paths in 0.42 s on average. The integration of LiDAR-based SLAM with deep-learning–driven semantic perception establishes a robust foundation for intelligent, adaptive, and safe robotic navigation in dynamic environments.

Keywords

RGB-D; semantic mapping; object recognition; obstacle avoidance; security robot

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