Smartphone-Based Indoor Localization within a 13th Century Historic Building

Abstract

Within this work we present an updated version of our indoor localization system for smartphones. The pedestrian’s position is given by means of recursive state estimation using a particle filter to incorporate different probabilistic sensor models. Our recently presented approximation scheme of the kernel density estimation allows to find an exact estimation of the current position, compared to classical methods like weighted-average. Absolute positioning information is given by a comparison between recent Wi-Fi measurements of nearby access points and signal strength predictions. Instead of using time-consuming approaches like classic fingerprinting or measuring the exact positions of access points, we use an optimization scheme based on a set of reference measurements to estimate a corresponding Wi-Fi model. This work provides three major contributions to the system. The most essential contribution is the novel state transition based on continuous walks along a navigation mesh, modeling only the building’s walkable areas. The localization system is further updated by incorporating a threshold-based activity recognition using barometer and accelerometer readings, allowing for continuous and smooth floor changes. Within the scope of this work, we tackle problems like multimodal densities and sample impoverishment (system gets stuck) by introducing different countermeasures. For the latter, a simplification of our previous solution is presented for the first time, which does not involve any major changes to the particle filter. The goal of this work is to propose a fast to deploy localization solution, that provides reasonable results in a high variety of situations. To stress our system, we have chosen a very challenging test scenario. All experiments were conducted within a 13th century historic building, formerly a convent and today a museum. The system is evaluated using 28 distinct measurement series on four different test walks, up to 310 m length and 10 min duration. It can be shown, that the here presented localization solution is able to provide a small positioning error, even under difficult conditions and faulty measurements. The introduced filtering methods allow for a real fail-safe system, while the optimization scheme enables an on-site setup-time of less then 120 min for the building’s 2500 m2 walkable area.

Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios

“…With measurements to several APs, the smartphone position can be found by intersecting the circles defined by (7). Two circles produce two intersection points, assuming that the AP positions are not equal and that an intersection exists.…”

“…Complex scenarios with many non-line-of-sight (NLOS) transmissions require an equally complex signal strength prediction model, taking into account the characteristics of the environment like the different attenuation coefficients of the building's walls [5,6]. The determination of such parameters is often only possible with a great deal of effort and material knowledge, especially for older buildings [7]. Furthermore, to determine a more or less realistic signal prediction for a location in question, intersection checks of each obstacle within the line-of-sight to the AP have to be calculated.…”

Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

“…Various upgrades may be performed to achieve better results in real scenarios. For instance, Fetzer et al [ 5 ] suggest to calculate the final estimated position at one of the modes of the particle clusters instead of the computed average location from all particles.…”

“…In ref. [ 5 ], a navigation graph and a mesh approach are proposed. The transition is performed only along these structures preventing the step transition to intersect any obstacle.…”

“…Pedestrian navigation in a building complex [ 1 ], guiding visually impaired visitors in a museum [ 2 ], helping patients to find a ward in a hospital [ 3 ], navigating a drone in a warehouse [ 4 ], positioning in a historical building [ 5 ], navigating a person on a wheelchair [ 6 ], orientating firefighters in a unknown indoor environment [ 7 ], and navigating cars in a parking garage [ 8 ] describe application examples of indoor navigation system including the indoor localization. The existence of various use cases determines assorted requirements on a positioning system.…”