SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

Abstract

Abstract-Indoor localization typically relies on measuring a collection of RF signals, such as Received Signal Strength (RSS) from WiFi, in conjunction with spatial maps of signal fingerprints. A new technology for localization could arise with the use of 4G LTE telephony small cells, with limited range but with rich signal strength information, namely Reference Signal Received Power (RSRP). 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 or for network deployment optimization. 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. SLAM has recently been extended to incorporate signal strength from WiFi 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, "from the pocket". Finally, we demonstrate the localization performance using selected single modalities, such as only WiFi and the WiFi signal maps that we generated.

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

“…However, we focus on solutions that have no prior knowledge of the indoor space and output primarily the physical map and optionally the measurements allow updating both user trajectory and environment map at two reference points PlaceSLAM RSS measurements provide proximity information relative to some well yes yes Average tracking error [185] recognizable places, e.g. doors 2-10 m in a 10 min walk SignalSLAM Wi-Fi/Bluetooth RSS, 4G LTE RSRP, magnetic field, GPS reference locations, yes yes Median tracking error [186] NFC tag or QR code readings at landmarks, and PDR based on IMU data 11-14 m DPSLAM Distributed particle filter to constrain the drift of a hip-mounted smartphone yes yes Localization error 3 m [187] IMU, user needs to revisit locations periodically for enabling loop closure at final location FEKFSLAM Low complexity SLAM approximation, maintains only a single hypothesis of yes yes Localization error 4 m [188] the state, requires a loop closure detection step at every measurement epoch at final location SmartSLAM Switches between DPSLAM, FEKFSLAM and other fusion algorithms to yes yes Depends on the [188] reduce complexity and save battery, while maintaining good accuracy scenario and algorithm [189] Uses IMU and a foot-mounted piezoelectric sensor to estimate the lengths and yes X Relative error 3% (length) orientations of the hallways for relative floor mapping and 4 • (orientation) CIMLoc Uses crowdsourced data from smartphone IMU sensors to derive users' yes X Average map error < 0.4 m [190] trajectories with PDR and particle filter compared to true map MapGENIE Uses foot-mounted IMU data to generate the hallways and processes them to yes X Correctly detects 88% of the [191] estimate the remaining structure (e.g., geometry of rooms and their areas) hallways and 81% of the rooms Walkie-Markie…”

“…and PDR based on IMU data [186]. This is essentially a modification of the WiFi GraphSLAM approach and the main difference is that the similarity in signal space conditions the proximity in physical space.…”

Rethinking Indoor Localization Solutions Towards the Future of Mobile Location-Based Services

“…They hope to use this "expected" position to recognize pre-known locations and are investigating the use of pattern matching techniques, such as Fuzzy Logic, to allow the system to associate live data with the stored results (Cook et al, 2005). The pedestrian dead reckoning (motion data from the smartphone) with position fixes (such as NFC tags) and the Simultaneous Localization and Mapping (SLAM) algorithm adapted for RF signal data (Mirowski et al, 2013). Google announced a new service to offer detailed indoor location positioning using its Tango 3D sensing computer vision technology, called Vision Positioning Service (VPS).…”

“…Simultaneous Localization and Mapping (SLAM) has recently been extended to incorporate signal strength from WiFi in the socalled WiFiSLAM algorithm (URL 1).Another approaches WiFiGraphSLAM based on the extension to WiFi of the GraphSLAM algorithm and RFSLAM (SignalSLAM) based on the extension to RSS for WiFi and Bluetooth, and Reference Signal Received Power (RSRP) for small cells of the GraphSLAM algorithm (Mirowski et al, 2013).…”

A hybrid BLE and Wi-Fi localization system for the creation of study groups in smart libraries

“…The authors also propose different system architectures, depending on where the position estimation is performed (on the device or on the server) and evaluate them with respect to client-side energy consumption, delay and coexistence of Bluetooth and WiFi. Other examples of hybrid Wi-Fi and Bluetooth systems can be found in the research papers [13] and [14]. All these systems report improvements in the accuracy of the position estimation compared to Wi-Fi only solutions, generally lower than 3 meters on average.…”