Position estimation experiment on bioinspired robotic mouse with inertial measurement unit

This paper presents an empirical study of position estimation for the small bioinspired robotic mouse TUCmouse. The objective was to integrate a low-cost inertial measurement unit (IMU) into the robot and evaluate whether approximate indoor position estimation can be achieved without external onboard localization sensors. Although established dead-reckoning and legged-robot state-estimation methods exist, applying them to a small, lightweight robot introduces additional challenges related to scale, sensor noise, body dynamics, and the lack of direct foot-contact sensing. Despite these challenges, this study evaluates three simplified approaches: orientation-aided step counting, foot-kinematic estimation, and an EKF-based method using IMU prediction with covariance-weighted kinematic and zero-velocity pseudomeasurements. Experiments on wooden and elastomeric polymer surfaces show that the EKF-based method achieves the lowest endpoint error among the tested methods. The results demonstrate both the potential and the limitations of low-cost inertial sensing for short-range approximate localization on small bioinspired robots.