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

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.