Probabilistic localization based on Bayesian theory has been researched as a sensor fusion method to improve the robustness of localization. Pieces of position information, generated by sensors’ observation models with consideration for noises, are fused according to Bayesian theory. However, having large noises not considered in their observation models, the sensors output erroneous position information; thus, the fusion result has a significant error, even when the other sensors output correct ones. In this research, we have proposed a sensor fusion system with a relative correlation checking test to realize robust localization. Pieces of erroneous position information, biased against others and having a negative correlation with others, are detected and excluded in our proposed system by checking their correlation between all of them. The purpose of this paper is to evaluate the robustness of our fusion system by conducting recursive localization experiments in various environments.

中文翻译：

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具有相关性检查测试的各种环境中的多传感器鲁棒定位

研究了基于贝叶斯理论的概率定位技术，以提高定位的鲁棒性。根据贝叶斯理论融合传感器观测模型在考虑噪声的情况下生成的位置信息。但是，由于观测模型中未考虑大噪声，因此传感器输出错误的位置信息。因此，即使其他传感器输出正确的传感器，融合结果也会产生明显的误差。在这项研究中，我们提出了一种具有相对相关性检查测试的传感器融合系统，以实现鲁棒的定位。在我们提出的系统中，通过检查所有位置之间的相关性，可以检测并排除错误的位置信息，这些错误的位置信息会偏向他人并与他人具有负相关性。