The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVIII-1/W1-2023
25 May 2023
 | 25 May 2023


M. Orfanos, H. Perakis, and V. Gikas

Keywords: Underground Quarry, Fingerprinting, FTM, WiFi, LoRa, BLE, Indoor Positioning, Technology Characterization

Abstract. Mining and quarrying industry has recently made a shift towards underground exploitation as a viable alternative to traditional open-pit approaches. Thus, emerged the imperative need for localization systems for personnel safety and operations’ monitoring purposes. While there are many approaches taking advantage of the various signals of opportunity (SoO) supported by Internet of Things (IoT) for indoor and underground navigation, the need for a GNSS alternative in such areas is still present in terms of meeting system and user requirements (scale, cost, availability, accuracy, and integrity). The goal of this research is to provide insights regarding different Radio Frequency (RF) technologies operation and evaluate their positioning capabilities (Wi-Fi, BLE and LoRa) in underground industrial facilities such as quarries and mines, following and expanding the tests of previous studies in controlled environment. Furthermore, the multi-sensory approach that this study is pursuing, aims to provide the foundations of a low-cost, scalable and robust positioning system. This system would integrate the characteristics of the aforementioned technologies in order to meet the application-specific user requirements and set the basis for a more efficient mobile mapping system. In this context, technologies’ characterization and comparison is presented, by using data from a real operating underground quarry. The data gathered lead to the conceptualization of the localization scheme, which besides the SoO observables, utilizes their availability status as an additional feature within the quarry as well. The proposed combined approach outperformed the rest, achieving an accuracy bellow 15m for the 85% of the test data, which is sufficient for typical quarrying operations monitoring and management requirements.