SignalSLAM: Simultaneous Localization and Mapping with Mixed WiFi, Bluetooth, LTE and Magnetic Signals

Indoor localization, a key enabler for pervasive computing and network optimization, relies on measuring a collection of RF signals. On a typical smartphone, indoor localization in GPS-deprived areas can be determined using round-trip delay or Received Signal Strength (RSS) fingerprints from GSM or CDMA cellular network signal and from WiFi. While more accurate, WiFi fingerprinting requires building precise signal maps. A new technology for localization arises with the use of 4G LTE small cells with limited range (that can be comparable to the coverage of a WiFi access point) but richer information about the signal strength, consisting in Reference Signal Received Power and Quality (RSRP and RSRQ). In this paper, we propose to combine an ensemble of available sources of RF signals to build multi-modal signal maps that can be used for localization. We primarily rely on Simultaneous Localization and Mapping (SLAM), which provides a solution to the challenge of building a map of observations without knowing the location of the observer. It is a well-known problem in robotics, where observations typically come from a laser range sensor or from monocular or stereoscopic cameras, and where the robot follows a motion model measureable by wheel encoders and inertial sensors. It has recently been extended to incorporate signal strength from WiFi in lieu of optical observations in the so-called WiFi-SLAM. In parallel to WiFi-SLAM, other localization algorithms have been developed that exploit the inertial motion sensors and a known map of either WiFi RSS or of magnetic field magnitude. In our study, we use all the measurements that can be acquired by an off-the-shelf smartphone and crowd-source the data collection from several experimenters walking freely through a building, collecting time-stamped WiFi and Bluetooth RSS, 4G LTE RSRP, magnetic field magnitude, GPS reference points when outdoors, Near-Field Communication (NFC) readings at specific landmarks and pedestrian dead reckoning based on inertial data. We resolve the location of all the users using a modified version of Graph-SLAM optimization of the users' poses with a collection of absolute location and pairwise constraints that incorporates multi-modal signal similarity. We demonstrate that we can recover the user positions and thus simultaneously generate dense signal maps for each WiFi access point and 4G LTE small cell. Finally, we demonstrate the localization performance using selected single modalities, such as only WiFi and the WiFi signal maps that we generated.