Weak Calibration of a Visible Light Positioning System Based on a Position-Sensitive Detector: Positioning Error Assessment

De-La-Llana-Calvo, Álvaro; Lázaro-Galilea, José-Luis; Gardel, Alfredo; Salido-Monzú, David; Bravo, Ignácio; Iamnitchi, Andreea; Gil-Vera, Ruben

doi:10.3390/s21113924

Cited by 12 publications

(4 citation statements)

References 53 publications

(70 reference statements)

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“…The proposed method obtains an average 2D position error < 4.5 cm, without prior knowledge of the system parameters. In [117] apply GA to obtain a low-complexity, weak calibration for an indoor VLP system. The suggested calibration only needs six measurement locations inside the environment, and via the GA the calibration constants are obtained, without knowing the system characteristics beforehand.…”

Section: ) Eas In Vlp Formulationsmentioning

confidence: 99%

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Rekkas,

Iliadis,

Sotiroudis

et al. 2023

IEEE Open J. Commun. Soc.

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Section: ) Eas In Vlp Formulationsmentioning

confidence: 99%

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Rekkas,

Iliadis,

Sotiroudis

et al. 2023

IEEE Open J. Commun. Soc.

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“…Indoor localization can be done using several possible wireless technologies ranging from Bluetooth [1]- [3], Wi-Fi [4], Ultra-Wideband (UWB) [5], [6], ZigBee [7], [8], visible light [9] to millimetre wave radar [10] and others employing computer vision [11] or dead reckoning [6], [12] which can work without depending on previously created radio infrastructure. Each of the technologies has its own advantages and disadvantages.…”

Section: A Technologies For Indoor Positioning Systemsmentioning

confidence: 99%

Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms

et al. 2023

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Indoor positioning and navigation increasingly has become popular and there are many different approaches, using different technologies. In nearly all of the approaches the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation Radio Map (RM) by analysing the environment. As this is usually done on a regular grid, the collection of Received Signal Strength Indicator (RSSI) data at every Reference Point (RP) of a RM is a time consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful as it allows researchers to spend more time with research instead of data collection. In this paper we analyse the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using 5 ESP32 micro controllers working in monitoring mode and placed around our office. We then analyse the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian Process Regression (GPR) to find balance between final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.

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“…If the receptor is a photodetector, it often consists of an array of photodiodes [13]. The most common photoreceivers used in visible and near infrared light detection are position-sensitive detectors (PSD) in combination with a lens [14,15] and quadrant photodiode angular diversity aperture (QADA) with an aperture lens [16,17]. In these sensors, the perspective camera model (also known as the pin-hole camera) is an accurate geometric model to relate the beacon's 3D position with the sensor's 2D measurement.…”

Section: Introductionmentioning

confidence: 99%

Using Perspective-n-Point Algorithms for a Local Positioning System Based on LEDs and a QADA Receiver

Aparicio-Esteve

Ureña

Hernández

et al. 2021

Sensors

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The research interest on location-based services has increased during the last years ever since 3D centimetre accuracy inside intelligent environments could be confronted with. This work proposes an indoor local positioning system based on LED lighting, transmitted from a set of beacons to a receiver. The receiver is based on a quadrant photodiode angular diversity aperture (QADA) plus an aperture placed over it. This configuration can be modelled as a perspective camera, where the image position of the transmitters can be used to recover the receiver’s 3D pose. This process is known as the perspective-n-point (PnP) problem, which is well known in computer vision and photogrammetry. This work investigates the use of different state-of-the-art PnP algorithms to localize the receiver in a large space of 2 × 2 m2 based on four co-planar transmitters and with a distance from transmitters to receiver up to 3.4 m. Encoding techniques are used to permit the simultaneous emission of all the transmitted signals and their processing in the receiver. In addition, correlation techniques (match filtering) are used to determine the image points projected from each emitter on the QADA. This work uses Monte Carlo simulations to characterize the absolute errors for a grid of test points under noisy measurements, as well as the robustness of the system when varying the 3D location of one transmitter. The IPPE algorithm obtained the best performance in this configuration. The proposal has also been experimentally evaluated in a real setup. The estimation of the receiver’s position at three particular points for roll angles of the receiver of γ={0°, 120°, 210° and 300°} using the IPPE algorithm achieves average absolute errors and standard deviations of 4.33 cm, 3.51 cm and 28.90 cm; and 1.84 cm, 1.17 cm and 19.80 cm in the coordinates x, y and z, respectively. These positioning results are in line with those obtained in previous work using triangulation techniques but with the addition that the complete pose of the receiver (x, y, z, α, β, γ) is obtained in this proposal.

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Weak Calibration of a Visible Light Positioning System Based on a Position-Sensitive Detector: Positioning Error Assessment

Cited by 12 publications

References 53 publications

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms

Using Perspective-n-Point Algorithms for a Local Positioning System Based on LEDs and a QADA Receiver

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