Finite Memory-Simultaneous Localization and Calibration With Application to Mobile Robots

The movement of mobile robots can deviate from the control input due to factors such as wheel slip, drift, and unexpected motor loads, degrading the localization accuracy. To address this issue, a novel finite memory-simultaneous localization and calibration (FM-SLAC) is presented in this article. The proposed algorithm estimates any unknown component in the control input that is causing an error in the robot motion and then uses it to calibrate the localization results. The presented FM-SLAC has inherent robustness against modeling and computational errors due to its unique finite memory structure, allowing it to overcome the limitations of the existing algorithms. We conducted robot localization experiments in two scenarios where robot motion deviated from the control input to validate the performance of the proposed algorithm.