LayoutSLAM: Object Layout based Simultaneous Localization and Mapping for Reducing Object Map Distortion

There is an increasing demand for robots that can be substituted for humans in various tasks. Mobile robots are being introduced in factories, stores, and public facilities for carrying goods and cleaning. In factories and stores, desks and shelves are arranged such that the work and movement of personnel are reduced. The surrounding furniture is also set to ensure that a single task can be performed in the same place. It is essential to study the intelligence of robots using information from such layouts, wherein human labor and movements are optimized. However, There is no method of map construction or location estimation that uses the characteristics of furniture arrangements that facilitate human work in a work space. Therefore, this study proposes a method for object mapping using layouts in crowded workspaces. Graphically represent the characteristics of furniture placement that make it easy for people to work in a workspace. The links in the graph represent the connections between the objects in the layout property. The nodes are the objects, and the weights of the links represent the strength of the layout properties. This graph is optimized by GraphSLAM to construct a map that considers the arrangement's characteristics. Using the graph structure improves the map's accuracy while allowing for relative changes in placement. The results show a 50.44% improvement in accuracy in a space with 18 desks, followed by two variations of similar desk layouts. The same improvement in accuracy was also observed when the relative positioning of objects changed significantly in each variation, such as a change to the left or right on the same side.