Abstract
In this paper, we address a method for improving accuracy of a Neural Network (NN) aided Extended Kalman Filter (EKF) based SLAM by compensating for an odometric error of a robot. The NN is used for estimating the odometric error and online learning of NN is implemented by augmenting the synaptic weights of the NN as the elements of state vector in the EKF-SLAM process. Due to this trainability, the NN could adapt to systematic error of the robot without any prior knowledge and the proposed NN aided EKF-SLAM is very effective compared to the standard EKF-SLAM method under the colored noise or systematic bias error. Experimental results are presented to validate that our NN aided EKF-SLAM generates more accurate feature map than conventional EKF-SLAM.
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