Abstract
Autonomous navigation in unknown environments requires accurate simultaneous localization and mapping, reliable obstacle detection, and efficient path planning within a unified framework. This study proposes a real-time LiDAR-based SLAM-driven navigation system for mobile robots operating in structured indoor environments. The developed architecture integrates three-dimensional LiDAR sensing, ego-motion estimation, scan registration, loop closure optimization, and collision-aware trajectory planning to achieve robust environmental reconstruction and safe autonomous mobility. A probabilistic measurement model is employed to relate sensor observations to robot pose and map states, while back-end optimization mitigates cumulative drift and enhances global consistency. The navigation module incorporates obstacle segmentation and goal-directed path generation, ensuring smooth and collision-free trajectories under kinematic constraints. Experimental validation is conducted in both incremental and full-environment exploration scenarios using a physical robotic platform equipped with LiDAR and auxiliary sensors. Results demonstrate consistent mapping accuracy, stable trajectory estimation, and effective obstacle avoidance in cluttered indoor settings. The system maintains real-time computational performance while preserving the structural coherence of reconstructed environments. The findings confirm the reliability and scalability of the proposed framework, providing a practical foundation for autonomous robotic navigation in semi-structured and unstructured operational domains.