The development of cost-effective indoor autonomous robots critically depends on robust and efficient simultaneous localization and mapping (SLAM) capabilities. This paper presents a low-cost mobile robotic platform that integrates the Slamtec RPLIDAR A3 sensor with the open-source HectorSLAM algorithm to generate high-precision 2D occupancy grid maps in real time. The system is designed to operate reliably in GPS-denied environments such as laboratories, warehouses, and multi-level buildings, where traditional odometry sources, such as wheel encoders, inertial measurement units (IMUs), or GPS, may be absent or unreliable. Using exteroceptive LiDAR data exclusively, the proposed approach eliminates the need for complex sensor fusion and calibration procedures, thus reducing system complexity and deployment overhead. The hardware architecture is centered on an NVIDIA Jetson TX2 running Ubuntu 20.04 and ROS Noetic, with motion control managed by an Arduino Mega 2560 via the rosserial interface. LiDAR data are processed through the HectorSLAM package and visualized in RViz using standard ROS networking. Experimental validation in a controlled Quanser studio environment demonstrates mapping accuracy that exceeds 90 %. This work will be implemented on an autonomous mobile robot for indoor waypoints following and obstacle avoidance. These results confirm the feasibility of implementing an affordable and calibration-free SLAM framework for autonomous indoor robotic applications.