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首页> 外文期刊>IEEE Transactions on Industrial Electronics >A Square Root Unscented FastSLAM With Improved Proposal Distribution and Resampling
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A Square Root Unscented FastSLAM With Improved Proposal Distribution and Resampling

机译：平方根无味FastSLAM，具有改进的提案分配和重新采样

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摘要

An improved square root unscented fast simultaneous localization and mapping (FastSLAM) is proposed in this paper. The proposed method propagates and updates the square root of the state covariance directly in Cholesky decomposition form. Since the choice of the proposal distribution and that of the resampling method are the most critical issues to ensure the performance of the algorithm, its optimization is considered by improving the sampling and resampling steps. For this purpose, particle swarm optimization (PSO) is used to optimize the proposal distribution. PSO causes the particle set to tend to the high probability region of the posterior before the weights are updated; thereby, the impoverishment of particles can be overcome. Moreover, a new resampling algorithm is presented to improve the resampling step. The new resampling algorithm can conquer the defects of the resampling algorithm and solve the degeneracy and sample impoverishment problem simultaneously. Compared to unscented FastSLAM (UFastSLAM), the proposed algorithm can maintain the diversity of particles and consequently avoid inconsistency for longer time periods, and furthermore, it can improve the estimation accuracy compared to UFastSLAM. These advantages are verified by simulations and experimental tests for benchmark environments.
机译：本文提出了一种改进的平方根无味快速同时定位与制图（FastSLAM）。所提出的方法直接以Cholesky分解形式传播和更新状态协方差的平方根。由于提案分配的选择和重采样方法的选择是确保算法性能的最关键问题，因此通过改进采样和重采样步骤来考虑其优化。为此，使用粒子群优化（PSO）来优化提案分配。 PSO导致粒子集在权重更新之前趋向于后验的高概率区域。因此，可以克服颗粒的贫乏。此外，提出了一种新的重采样算法以改善重采样步骤。新的重采样算法可以克服重采样算法的缺陷，同时解决退化和样本贫困问题。与无味FastSLAM（UFastSLAM）相比，该算法可以保持粒子的多样性，从而避免了较长时间的不一致，并且与UFastSLAM相比，可以提高估计精度。这些优势已通过针对基准环境的仿真和实验测试得到了验证。

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