Pose Estimation of Mobile Robots Based on Maximum Correntropy Under Kalman Filtering Framework

Abstract

To address the problem of low pose estimation accuracy of traditional filtering algorithm for mobile robots in non-Gaussian noises, a pose estimation algorithm based on the combination of iterative unscented Kalman filter (IUKF) and maximum correntropy (MC), named as MCIUKF, was proposed for the application of simultaneous localization and mapping(SLAM). MC is used to deal with non-Gaussian noise, a cost function based on MC is constructed, and then the Levenberg-Marguardt method (LM) is used to optimize the cost function. On this basis, the iterative updating process of state and covariance is derived, and the updating steps of IUKF are improved. The simulation shows that the proposed algorithm has better estimation accuracy than traditional filtering algorithm in the non-Gaussian noise environments, and has favorable stability.

Keywords

• simultaneous localization and mapping
• non-gaussian noise
• iterative unscented kalman filter
• maximum correntropy
• cost function
• levenberg-marguardt method