Spatial Graph-Based Localization and Navigation on Scaleless Floorplan

Effective navigation in unfamiliar environments remains a critical challenge for successful deployment. Current navigation methods, which rely on autonomous or teleoperated exploration and map building, pose technical difficulties for inexperienced end-users. In contrast, humans can effectively navigate using abstract floorplans, suggesting the potential for service robots to leverage similar techniques. The practical application of floorplan-based navigation, however, is currently limited by methods that require pre-exploration or floorplan with accurate measurements or scale. This paper aims to address the aforementioned challenges and investigate the feasi- bility of floorplan-based navigation in unfamiliar environments. Specifically, we propose a novel scale-invariant floorplan local- ization method, enabling navigation without relying on precise scale information. Furthermore, we introduce an incremen- tal graph augmentation approach that enriches the floorplan representation with traversability information derived from robot observations. Finally, we develop an efficient navigation framework capable of utilizing both the inherent structure of the floorplan and real-time observations. Experimental re- sults demonstrate that our scale-invariant floorplan localization method outperforms baseline methods in most cases when floorplan scale information is unavailable, and our graph- based navigation system exhibits superior success and efficiency as compared to grid-based counterparts. The outcomes of this research contribute to the advancement of service robot deployment in unfamiliar environments, particularly in sce- narios where extensive exploration and map building may be impractical or technically challenging for end-users.