Indoor Positioning Technologies Without Offline Fingerprinting Map: A Survey

Abstract

Fingerprint-based wireless indoor positioning approaches are widely used for location-based services because wireless signals, such as Wi-Fi and Bluetooth, are currently pervasive in indoor spaces. The working principle of fingerprinting technology is to collect the fingerprints from an indoor environment, such as a room or a building, in advance, create a fingerprint map, and use this map to estimate the user's current location. The fingerprinting technology is associated with a high level of accuracy and reliability. However, the fingerprint map must be entirely re-created, not only when the Wi-Fi access points are added, modified, or removed, but also when the interior features, such as walls or even furniture, are changed, owing to the nature of the wireless signals. Many researchers have realized the problems in the fingerprinting technology and are conducting studies to address them. In this paper, we review the indoor positioning technologies that do not require the construction of offline fingerprint maps. We categorize them into simultaneous localization and mapping; inter/extrapolation; and crowdsourcing-based technologies, and describe their algorithms and characteristics, including advantages and disadvantages. We compare them in terms of our own parameters: accuracy, calculation time, versatility, robustness, security, and participation. Finally, we present the future research direction of the indoor positioning techniques. We believe that this paper provides valuable information on recent indoor localization technologies without offline fingerprinting map construction.

Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization

“…There are two main types of Wi-Fi-based indoor positioning technologies: the received signal strength indicator (RSSI)-based ranging positioning algorithm [12][13][14] and the fingerprint-based positioning algorithm [15][16][17]. The RSSI-based ranging positioning algorithm usually adopts the received Wi-Fi signal to estimate the distance between the target (its location is unknown) and the access point (its location is known) using the wireless radio signal propagation model and then estimates the target position using trilateration or multilateration methods.…”

“…Fingerprint-based positioning methods were explored in a machine learning-based method that was developed in [15], where the support vector machine (SVM) was used to determine the different postures of the user. Jang et al [16] provided an explicit survey for the limitation of an offline fingerprint map and overcame it with simultaneous localization and mapping (SLAM) methods. Meanwhile, Guan et al [17] introduced a heuristic method to detect anomalous fingerprints under the framework of probabilistic fingerprint-based indoor positioning.…”

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

“…In the offline phase, a site survey is conducted to collect the value of the received signal strength indicator (RSSI) at many reference points (RPs) from all the detected access points (APs). Some researchers have proposed using indoor positioning technologies that do not require the construction of offline fingerprint maps [36] or just by updating the maps automatically [37]. In the online phase, a user samples or measures an RSSI vector at his/her position.…”

Fusion of the SLAM with Wi-Fi-Based Positioning Methods for Mobile Robot-Based Learning Data Collection, Localization, and Tracking in Indoor Spaces

“…Unlike wireless technologies, such as Bluetooth, which require additional infrastructure construction, Wi-Fi is already installed in many residential or commercial spaces, so no additional installation is required. In Wi-Fi-based positioning, Wi-Fi fingerprinting technology collects and uses the strength of Wi-Fi signals at a specific location [ 8 , 9 ]. Wi-Fi fingerprinting technology utilizes the received signal strength (RSS) of Wi-Fi signals collected in an indoor space.…”