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On SLAM algorithm combined with entropy probability DDF II

机译:结合熵概率DDF的SLAM算法II

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In the view of the traditional linearization of nonlinear function in the inaccurate filtering process, the second order striling polynomial interpolation method was proposed to approximate the state quantity digital characteristics. The control input was redefined in this paper. Combined with the second order interpolation, it implemented the optimal control. The entropy probability filtering algorithm mentioned in this paper provides a new idea for the filtering process applied in SLAM. The simulation experiments showed that the second order interpolation filtering algorithm had a higher filtering precision, and the new information mean and variance tended to be zero with a faster convergence rate when the system was stable. The mean of ranging and angle measured of the new information based on entropy probability with divided difference filter II (DDF II) could be reached to stability as short as in two minutes, which were controlled in 0.1 m and 0.3 m.
机译:鉴于传统的非线性函数在不精确滤波过程中的线性化问题,提出了一种二阶细化多项式插值方法来近似状态量的数字特性。本文重新定义了控制输入。结合二阶插值,实现了最优控制。本文提出的熵概率滤波算法为SLAM中的滤波过程提供了新思路。仿真实验表明,二阶插值滤波算法具有较高的滤波精度,当系统稳定时,新的信息均值和方差趋于零,收敛速度更快。利用分差滤波器II(DDF II)基于熵概率测得的新信息的测距和角度平均值可以在短短两分钟内达到稳定,并控制在0.1 m和0.3 m范围内。

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