Reliable Indoor Localization in Multibuilding Environments: Leveraging Environment-Invariant and Position-Related Features

Received signal strength indicator (RSSI)-based indoor localization offers a cost-effective solution for autonomous mobile robot navigation in 3-D indoor environments, including cross-floor and multibuilding structures. However, localization accuracy is fundamentally constrained by the low sampling density and unstable measurement of RSSI data. So far, existing methods neglect cross-environment RSSI coherence (e.g., repeated signal patterns in geometrically similar areas), resulting in unreliable fingerprint databases. What is more, most approaches fail to model the spatial hierarchy of buildings, floors, and coordinates, which leads to lower accuracy in indoor localization model predictions. To address these issues, we propose EP-3DLoc, a novel 3-D indoor localization framework that combines an environment-invariant feature-based data completion (EIC) method with a position-related feature-based localization (PRL) method. The EIC enhances data quality by filling in sparse RSSI data using environment-invariant features, which are recurring RSSI patterns found in similar environmental structures. The PRL module combines multiscale RSSI signal processing (raw data and image-like data) with a multitask network that analyzes location relationships, enhancing localization accuracy in 3-D environments. Experimental results on public datasets (TUT2018, UTSIndoorLoc, and UJIIndoorLoc) have demonstrated that EP-3DLoc achieves state-of-the-art performance on indoor localization in multibuilding environments. Further testing on the self-constructed dataset HZAUIndoorLoc have revealed that EP-3DLoc not only outperforms existing methods in localization accuracy but also maintains low energy consumption and strong resistance to interference. The self-constructed dataset HZAUIndoorLoc is available at https://github.com/Hanzoe/HZAUIndoorLoc-Dataset.