Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing

Abstract

Anomalies of the omnipresent earth magnetic (i.e., geomagnetic) field in an indoor environment, caused by local disturbances due to construction materials, give rise to noisy direction sensing that hinders any dead reckoning system. In this paper, we turn this unpalatable phenomenon into a favorable one. We present Magicol, an indoor localization and tracking system that embraces the local disturbances of the geomagnetic field. We tackle the low discernibility of the magnetic field by vectorizing consecutive magnetic signals on a per-step basis, and use vectors to shape the particle distribution in the estimation process. Magicol can also incorporate WiFi signals to achieve much improved positioning accuracy for indoor environments with WiFi infrastructure. We perform an in-depth study on the fusion of magnetic and WiFi signals. We design a two-pass, bidirectional particle filtering process for maximum accuracy, and propose an on-demand WiFi scan strategy for energy savings. We further propose a compliant-walking method for location database construction that drastically simplifies the site survey effort. We conduct extensive experiments at representative indoor environments, including an office building, an underground parking garage, and a supermarket in which Magicol achieved a 90 percentile localization accuracy of 5m, 1m, and 8m, respectively, using the magnetic field alone. The fusion with WiFi leads to 90 percentile accuracy of 3.5m for localization and 0.9m for tracking in the office environment. When using only the magnetism, Magicol consumes 9× less energy in tracking compared to WiFibased tracking.

Last-Mile Navigation Using Smartphones

“…Due to its global availability and stability, the geomagnetic field is used in several indoor localization systems [13][14][15][21][22][23]. Most of them exploit magnetic field anomalies caused by the local disturbances of ferromagnetic building materials, and localize users/devices based on a pre-established fingerprint map.…”

“…If vSteps is much less than uSteps, the row number increases (line 9); if uSteps is much less than vSteps, the column number increases (line 12). If the step condition is satisfied, the algorithm sets the forward direction based on the position of the minimal similarity scores (lines [14][15][16][17][18][19][20][21][22]. If the local minimal cost is achieved at D[pv][pu], both the row and the column numbers increase.…”

“…This way, we can measure the offset between the follower's true locations and the locations obtained by the matching algorithm. For the purpose of comparison, we also implemented tracking algorithms using the geomagnetic field or WiFi as benchmarks [21]. Both approaches take a fingerprint map and the floor plan as inputs, and use particle filtering (PF) to continuously estimate the device's location.…”

“…Grand et al [22] proposed a lightweight magnetic map construction method and used an online particle filter to estimate the location of a handheld device. Similarly, a particle-filtering-based engine was designed in [21] to localize and track users with a given geomagnetic fingerprint map. In general, these techniques require special hardware [13,[24][25][26][27] or dense samples of magnetic data to build a fingerprint map with high training overhead [13,21,23,42].…”

Positioning and guiding educational robots by using fingerprints of WiFi and RFID array

“…Positioning educational robots is one of important tasks, because it is the critical technology for robots to provide the intelligent services. Accurating and pervasiving indoor positioning can significantly improve our everyday life [2]. Educational robots must have positioning ability so that they move to the target locations to provide their services [3].…”