Autonomous driving and navigation in new environments are one of the main challenges in modern vehicles because of the traditional locomotion and kinematics of these conventional vehicles. With this motivation, this research introduces an Omni-Directional Multi-Sensor Map Building System to advance autonomous vehicles and robotics applications by leveraging multiple sensing technologies. The system integrates data from a LiDAR sensor and an Intel RealSense RGB-D camera, employing advanced SLAM (Simultaneous Localization and Mapping) techniques to enhance mapping accuracy, environmental perception, and obstacle detection. A novel data fusion strategy effectively combines depth information from the camera with point cloud data from LiDAR, resulting in superior map resolution and robust environmental awareness. Designed for challenging environments, the system maintains accurate localization and mapping even in feature-sparse areas, poorly lit spaces, or conditions with reflective surfaces, dust, or fog. The LiDAR’s planar data strengthens obstacle detection in cluttered environments, complementing visual sensor capabilities to ensure consistent performance. The vehicle’s platform, powered by a Jetson Xavier Nx board, processes sensor data in real-time and communicates with a microcontroller-based DC motor control system via ROS (Robot Operating System). Equipped with four 45-degree Swedish Wheels, the platform offers omnidirectional holonomic architecture, enabling smooth navigation over uneven terrain, narrow passages, and confined spaces. Real-time processing allows the system to adjust speed and direction based on fused sensor data, enhancing adaptability to complex and rapidly changing environments. Extensive testing demonstrates the system’s ability to generate high-resolution maps and maintain accurate localization in conditions that challenge traditional SLAM methods. This work highlights the benefits of multi-sensor integration for advanced SLAM and navigation, contributing to the development of intelligent vehicle systems capable of operating efficiently and reliably in extreme operational environments.