Neural extended Kalman filter for monocular SLAM in indoor environment

Abstract

The extended Kalman filter (EKF) has become a popular solution for the simultaneous localization and mapping (SLAM). This paper presents the implementation of the EKF coupled with a feedforward neural network for the monocular SLAM. The neural extended Kalman filter (NEKF) is applied online to approximate an error between the motion model of the mobile robot and the real system performance. Inadequate modeling of the robot motion can jeopardize the quality of estimation. The paper shows integration of EKF with feedforward neural network and simulation analysis of its consistency and implementation of the NEKF with a mobile robot, laboratory experimental environment, and a simple USB camera. The simulation and experimental results show that integration of neural network into EKF prediction–correction cycle results in improved consistency and accuracy.