This paper delves into the initial phase of developing an autonomous navigation system for mobile platforms. It examines the impact of wheel velocity measurements on odometry estimation, considering the transition from a reliable and precise signal to noisy and less dependable information. The study investigates how this range of available data influences the accuracy of pose estimation for a ground vehicle. Additionally, the paper explores the incorporation of the Kalman Filter as a supplementary tool to enhance the input data quality, resulting in a consistent and reliable estimation of the ground vehicle's position and orientation. The findings presented herein contribute to the advancement of autonomous navigation systems, providing valuable insights into improving odometry estimation for mobile ground vehicles.