Dead Reckoning Navigation System for Autonomous Mobile Robot using Indirect Feedback Kalman Filter

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In this paper, a dead reckoning navigation system for differential drive mobile robots is presented. The navigation system consists of two incremental encoders and a gyroscope. We have built a third order polynomial function for compensating the nonlinear scale factor errors of the gyroscope. We utilize an indirect Kalman filter that feeds back estimated errors to the main navigation system. Also, the observability of the filter is analyzed in order to systematically evaluate the filter's performance. Experimental results show that the proposed navigation system provides a reliable position and heading angle by mutually compensating the encoder and the gyroscope errors. The proposed filter also reduces the computational burden and enhances the navigation system's reliability. The observability analysis confirms the characteristics of inevitably unbounded position error growth in dead reckoning navigation systems.