Abstract
This study aims to investigate autonomous location and environment mapping of moving objects under conditions of dust and weak illumination in underground tunnels. The standard extended Kalman filter (EKF) algorithm has a issue that system noise and the prior statistical characteristics of the observed noise cannot be accurately predicted. Thus, we propose an improved EKF algorithm to perform fuzzy adaptive simultaneous localization and mapping (SLAM). Laser matching is added to EKF prediction phase to predict the position, and the weighted average position is used as the final position of the predicted part. By observing the change of the mean value and covariance, the system noise and the weighted value of the observed noise are fuzzy adjusted. The improved filtering algorithm is applied to a SLAM simulation experiment, and the influence of four different landmark arrangements on position estimation is considered. The results show that the positioning and composition accuracy can be improved using the new algorithm, and the accuracy of positioning and composition is increased by more than 53.8% compared with standard EKF in y-direction. It is also found that a landmark layout along the center line of a roadway roof is superior to other arrangement methods.
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