Adopting the FAB-MAP Algorithm for Indoor Localization with WiFi Fingerprints

Abstract

Abstract. Personal indoor localization is usually accomplished by fusing information from various sensors. A common choice is to use the WiFi adapter that provides information about Access Points that can be found in the vicinity. Unfortunately, state-of-the-art approaches to WiFi-based localization often employ very dense maps of the WiFi signal distribution and require a time-consuming process of parameter selection. On the other hand, camera images are commonly used for visual place recognition, detecting whenever the user observes a scene similar to the one already recorded in a database. Visual place recognition algorithms can work with sparse databases of recorded scenes and are in general simple to parametrize. Therefore, we propose a WiFi-based global localization method employing the structure of the well-known FAB-MAP visual place recognition algorithm. Similarly to FAB-MAP, our method uses Chow-Liu trees to estimate a joint probability distribution of re-observation of a place given a set of features extracted at places visited so far. However, we are the first who apply this idea to recorded WiFi scans instead of visual words. The new method is evaluated on the UJIIndoorLoc dataset used in the EvAAL competition, allowing a fair comparison with other solutions.

Topological localization using Wi-Fi and vision merged into FABMAP framework

“…For example, corridors on opposite sides of a building, or at different floors, have different Wi-Fi signatures but can share comparable appearances. Recent work has introduced a way of including Wi-Fi data in the FABMAP algorithm [4] (preprint), but it does not benefit from advantages of both sensors.…”

Vision and Wi-Fi fusion in probabilistic appearance-based localization

“…For example, corridors on opposite sides of a building, or at different floors, have different Wi-Fi signatures but can share comparable appearances. Recent work has introduced a way of including Wi-Fi data into the FABMAP algorithm (Wietrzykowski et al, 2017), but it does not benefit from the advantages of both sensors since it only considers Wi-Fi signals.…”

“…Weight of the number of Wi-Fi words used to describe RSSI coming from each AP for Wi-Fi localization. Results computed with exclusive representation are plotted over gray background and compared to results from the incremental approach proposed inWietrzykowski et al (2017).…”

Real-Time Visual Place Recognition for Personal Localization on a Mobile Device

“…But the PUT MC database is relatively short when considering real-life applications and the sequence matching time grows linearly with the number of images in the database. Therefore, the system can be applied either in small buildings or it requires additional information from an independent source in order to limit the search area to a single floor or a part of the building [38]. This might be an important factor when choosing between a typical place recognition algorithm (e.g.…”

“…containing images from several multi-floor buildings. Therefore, we recommend to use FastABLE and additionally divide a huge database into smaller sequences utilizing prior information from other sources [38].…”