用简单转换将机器人定位建模为线性系统，提升精度与稳定性。
Improvement of Robot's Simultaneous Localization and Mapping Using an Effective Transformation to Achieve Linear Model
- 通过指南针和有效变换将非线性状态模型转为线性模型
- 相比EKF-SLAM在精度、收敛性和计算复杂度上显著更优
- 对传感器噪声和参数变化更鲁棒，适合实际部署
如今移动机器人广泛应用。同时定位与地图构建（SLAM）是其核心任务，主流方法基于扩展卡尔曼滤波（EKF）。EKF-SLAM的主要问题在于发散，源于运动与观测模型的非线性及线性化误差。已有改进尝试成效有限。本文通过引入简易指南针并应用有效变换，将非线性状态空间模型转化为线性模型，进而使用原始卡尔曼滤波（KF），提出新方法LMKF-SLAM。实验表明，该方法在精度、收敛性与计算复杂度上均显著优于现有先进方法，尤其优于EKF-SLAM。此外，对传感器不确定性与系统参数变化更具鲁棒性。
原文摘要 · Abstract (English)
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.
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