Self-adjusting Desensitized EKF-SLAM System for Autonomous Mobile Robot

SLAM has become the core method of robot positioning, but SLAM based on EKF method cannot solve the filter sensitivity problem caused by uncertain parameters. This paper proposed a self-adjusting desensitized EKF-SLAM system based on the Desensitised EKF which momentarily adjusts the emphasis on the sensitivity in the cost function. This algorithm utilizes the comparison of systematic theoretical residual and practical innovation to construct the weight factor of the sensitivity in the cost function, which adjusts the sensitivity weight online so as to realize the adaptive estimation of the desensitized Kalman filter. This method is applied to the EKF-SLAM system to overcome the degradation of system performance caused by systemic sensitivity to uncertain parameters from the odometer. Finally, the results from the outdoorsy experiment demonstrate the performance of the proposed desensitized EKF-SLAM system.