Mobile robots have become increasingly significant in numerous fields due to their mobility, autonomy, and capacity to carry out tasks in dynamic conditions. However, as we delve deeper into the field of robotics, we face a recurring challenge: maneuvering mobile robots to accurately determine their own position while simultaneously creating a map of their surroundings. This critical process is defined as Simultaneous Localization and Mapping (SLAM). A major challenge SLAM faces is the strategic selection of the optimal set of sensors to ensure the acquisition of precise and reliable results. This research paper presents a comprehensive exploration of the design and implementation of a mobile robotic platform meticulously engineered for SLAM applications. By integrating sensors like LiDAR (light detection and ranging), IMU (inertial measurement unit), and wheel encoders into a mecanum wheel robot, it perceived the environment more accurately and Raspberry Pi eliminated the need for Arduino for data processing. It is observed that the developed robot navigates efficiently and maps the environment precisely. The selection of mecanum wheel configuration helped in precise positioning, for enhanced maneuverability and versatility. However, the LiDAR’s restricted range hindered mapping in cluttered areas.