Autonomous Mobile Robots (AMRs) need accurate maps to make informed decisions and navigate in real-time. The Simultaneous Localization and Mapping (SLAM) technique allows robots to build maps while moving. However, SLAM can be challenging to use in complex or dynamic environments. This paper presents an autonomous mobile robot called Scramble, which uses multimodal SLAM based on the fusion of data from an RGB camera, an integrated RPLIDAR with odometry data obtained by two encoders attached to the robot’s front wheels, and an IMU (inertial measurement unit) positioned at its center of mass. How can the accuracy of mapping, navigation, and obstacle detection of autonomous mobile robots using data fusion be improved? In this paper, we show that the fusion of visual data with depth and odometry data collected by IMU and encoders significantly improves autonomous mobile robots’ mapping, navigation, and obstacle detection accuracy. This study contributes to the advancement of autonomous robot navigation by introducing a data fusion-based approach to SLAM. Autonomous mobile robots are used in various applications, such as package delivery, medicine, healthcare, sports, ergonomics, industry, goods distribution, service robotics, cleaning, and inspection. Developing more robust and accurate SLAM algorithms is crucial for using these robots in challenging environments.