Improvement of Robot's Simultaneous Localization and Mapping Using an Effective Transformation to Achieve Linear Model

2026-06-26

AI Summary

This work addresses the divergence issue in extended Kalman filter (EKF)-based SLAM caused by linearization errors inherent in nonlinear models. To overcome this limitation, the authors propose a compass-aided state-space transformation that exactly reformulates the original nonlinear SLAM problem into a linear form, thereby enabling the direct application of the standard Kalman filter (KF). This approach significantly enhances system stability, improves localization and mapping accuracy, and ensures better convergence while reducing computational complexity. Experimental results demonstrate that the proposed LMKF-SLAM outperforms conventional EKF-SLAM and other state-of-the-art methods in terms of accuracy, robustness, and computational efficiency.

Abstract

Nowadays mobile robots have wide engineering applications. Simultaneous localization and mapping (SLAM) is an important task of these robots. The major and common algorithms used for this task are based on extended Kalman filter (EKF). One of the main problems in EKF-based SLAM is its divergence. The nonlinearity of motion and observation models and linearization error are the main reasons for the divergence. There have been some efforts to address this problem with limited success. In this paper, by applying a simple compass and using an effective transformation, we transform the non-linear state space model into a linear model. Then, by applying the original KF to this model, we reach a new method, which is called LMKF SLAM. We show that the LMKF SLAM is significantly superior to the state-of-the-art methods, especially EKF-based SLAMs, both in accuracy, convergence, and computational complexity. The proposed method is also more stable with respect to the uncertainty of sensors values and changes in system parameters. Experimental results verify these points.

Problem

• SLAM
• EKF divergence
• nonlinearity
• linearization error
• mobile robots

Innovation

• Linearization
• Kalman Filter
• SLAM
• State Space Transformation
• Mobile Robotics