Real-Time LiDAR SLAM-Driven Navigation and Collision Avoidance for Mobile Robots in Unstructured Environments

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.

@article{Tuleshov2026,
  title     = {Real-Time LiDAR SLAM-Driven Navigation and Collision Avoidance for Mobile Robots in Unstructured Environments},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Amandyk Tuleshov and Anar Adilkhan and Moldir Kuatova and Gaukhar Seidaliyeva},
  doi       = {10.14569/IJACSA.2026.0170325},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170325}
}