Improvement of Robot's Simultaneous Localization and Mapping Using an Effective Transformation to Achieve Linear Model
A new method called LMKF SLAM is proposed, which uses a simple compass and an effective transformation to convert the nonlinear state-space model into a linear one, addressing divergence issues in EKF-based SLAM. Experiments show superior accuracy, convergence, and computational efficiency compared to state-of-the-art methods.
[2606.28475] Improvement of Robot's Simultaneous Localization and Mapping Using an Effective Transformation to Achieve Linear Model
[Submitted on 26 Jun 2026]
Title:Improvement of Robot's Simultaneous Localization and Mapping Using an Effective Transformation to Achieve Linear Model
View a PDF of the paper titled Improvement of Robot's Simultaneous Localization and Mapping Using an Effective Transformation to Achieve Linear Model, by Seyed Farzad Bahreinian and 3 other authors
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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.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.28475 [cs.RO]
(or arXiv:2606.28475v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.28475
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Maziar Palhang [view email] [v1] Fri, 26 Jun 2026 16:02:39 UTC (1,190 KB)
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