屋外環境下で用いる自律移動システムに関する研究：第59報

―景観評価によるVisual Quality値を用いた自己位置推定の検討―

抄録

In most cases, many sensors are used in autonomous robots for self-position estimation to acquire detailed coordinate values, create a dense map, and implement map-matching. However, though human being doesn’t use coordinate values, it is easy to head toward the destination. There are many parameters human being uses for self-position estimation. Among them, we focus on scenery change in this paper, and propose a method of self-position estimation using sparse scenery information. In order to detect scenery change, we quantitated scenery information by using SegNet and named the value VQ (Visual Quality). First, we verified that the similarity of a VQ wave would be high in the same rout. Next, we used VQ and classes of semantic segmentation, and SIFT (Scale-Invariant Feature Transform) to implement selfposition estimation. As a result, the accuracy was enough and VQ can be used as a parameter for self-position estimation.