未知障害物によるモンテカルロ自己位置推定の破綻を防ぐための観測範囲の制限と選択

抄録

We modify Monte Carlo localizaiton as it can eliminate noisy measurements from a LiDAR derived from unknown obstacles. This modified method makes each particle have its observation direction with a range smaller than that of the LiDAR. In the calculations of Bayes theorem and resampling, the directions of the observation are changed as particles do not observe unrecorded obstacles on the map for self-localization. We have examined this method in a simulator. The result suggests that a restriction of the observation range makes MCL stable at the expense of precision.