Environment-Guided Localization via Factor Graph Inference Under Obstructed Line-of-Sight Conditions

Indoor localization is a key technology for the safe operation of automatic guided vehicles (AGVs) in smart factories. The time-of-arrival (TOA) observations provide physically interpretable distance measurements, and TOA-based localization can be realized using existing wireless infrastructure but remains challenging due to the effects of multipath propagation and frequent obstruction of the line-of-sight (LOS). In a typical factory environment, the AGV and access point locations, LOS/non-LOS conditions, and obstacle layout are tightly coupled because they jointly determine the underlying radio propagation characteristics, which fundamentally affect the TOA-based localization performance. We propose an environment-guided localization framework that integrates the multiple dependencies of the propagation environment into a factor graph to reduce the uncertainty of TOA-based localization under multipath conditions. Factor graphs can be used to represent heterogeneous dependencies where each physical relationship is expressed as an individual factor. The factor graph for distributed localization comprises a TOA factor and positional constraint factor derived from a known obstacle map. We extend this factor graph to cooperative localization by adding an order-consistency (OC) factor that considers the environmental similarity among AGVs. Simulation results showed that the OC factor sharpens the marginal distributions of positions and provided insights into conditions under which the OC factor improves or degrades the accuracy of cooperative localization.