Improvement of Robot's Simultaneous Localization and Mapping Using an Effective Transformation to Achieve Linear Model
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...
| Authors | Seyed Farzad Bahreinian, Maziar Palhang, Mohammad Reza Taban et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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