“Accurate position estimation, especially in indoor environments, presents a formidable challenge despite significant advancements in estimation methodologies. The integration of additional sensors is often imperative to refine estimates, particularly within indoor settings. The choice of additional sensors depends on the specific applications and operational range. This study focuses on devising a position estimation framework utilizing the internal sensors such as the Inertial Measurement Unit (IMU) and wheel encoders within a scaled-down Electric Vehicle. The proposed approach entails a combination of crafting a digital filter and implementing a Kalman filter to effectively attenuate noise and errors in the IMU data, thereby enhancing the position estimate, and the methodology is validated through experiments. This work contributes to the broader discourse on advancing indoor position estimation while bypassing the need for external positioning systems.”