Visible Light Positioning Using Bayesian Filters

Amsters, Robin; Holm, Dimiter; Joly, Joren; Demeester, Eric; Stevens, Nobby; Slaets, Peter

doi:10.1109/jlt.2020.3006618

Cited by 21 publications

(25 citation statements)

References 32 publications

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“…This setup was also calibrated manually as a point of reference. In our previous work, we described sensor fusion based robot positioning with three filters, namely an Extended Kalman Filter (EKF), a Particle Filter (PF) and a hybrid Particle/Kalman filter (PaKa) [ 41 ]. We now use the parameters from both the manual and robot calibration in these positioning approaches.…”

Section: Resultsmentioning

confidence: 99%

“…We used all data from the conditions described in Section 4.2.3 . Positioning accuracy results were obtained in the same way as described in [ 41 ]. Contrary to previous sections, “positioning accuracy” does not refer to the accuracy on the position of the transmitters.…”

Section: Resultsmentioning

confidence: 99%

“…In order to evaluate the proposed calibration procedure, we used the same experimental setup as our previous works [ 13 , 41 ] (see Figure 2 ). Four VLP transmitters were mounted at a height of approximately 1.5 m. Light intensity of every transmitter was modulated by a Metal-Oxide Semiconductor Field-Effect Transistor (MOSFET) that was connected to a signal generator.…”

Section: Methodsmentioning

confidence: 99%

“…Note that not all VLP systems require a calibrated channel model. Camera-based implementations such as [ 41 ] only detect the relative position of the light to the camera center, while photodiode-based systems use the signal strength to obtain the transmitter–receiver distance. Camera-based VLP systems therefore only need the location of each transmitter.…”

Section: Related Workmentioning

confidence: 99%

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Calibration of Visible Light Positioning Systems with a Mobile Robot

Amsters

Demeester

Stevens

et al. 2021

Sensors

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Most indoor positioning systems require calibration before use. Fingerprinting requires the construction of a signal strength map, while ranging systems need the coordinates of the beacons. Calibration approaches exist for positioning systems that use Wi-Fi, radio frequency identification or ultrawideband. However, few examples are available for the calibration of visible light positioning systems. Most works focused on obtaining the channel model parameters or performed a calibration based on known receiver locations. In this paper, we describe an improved procedure that uses a mobile robot for data collection and is able to obtain a map of the environment with the beacon locations and their identities. Compared to previous work, the error is almost halved. Additionally, this approach does not require prior knowledge of the number of light sources or the receiver location. We demonstrate that the system performs well under a wide range of lighting conditions and investigate the influence of parameters such as the robot trajectory, camera resolution and field of view. Finally, we also close the loop between calibration and positioning and show that our approach has similar or better accuracy than manual calibration.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Calibration of Visible Light Positioning Systems with a Mobile Robot

Amsters

Demeester

Stevens

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many Indoor Positioning Systems (IPSs), whose development has significantly increased in the last two decades [ 1 , 2 , 3 ], strive to locate the user at all times and as accurately as possible. Different technologies such as Wi-Fi [ 4 , 5 ], Bluetooth Low Energy (BLE) [ 6 , 7 ], visible light [ 8 , 9 ] and ultrasound [ 10 , 11 ] have been extensively used in combination with positioning techniques that include triangulation, trilateration, multilateration, and fingerprinting, among others. Nevertheless, although such positioning can have many applications, in the vast majority of cases, users only need to know with some accuracy where they are and how to get to the desired Point of Interest (PoI).…”

Section: Introductionmentioning

confidence: 99%

Noise-Resilient Acoustic Low Energy Beacon for Proximity-Based Indoor Positioning Systems

Aguilera

Aranda

Parralejo

et al. 2021

Sensors

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Proximity-Based Indoor Positioning Systems (PIPSs) are a simple to install alternative in large facilities. Besides, these systems have a reduced computational cost on the mobile device of those users who do not continuously demand a high location accuracy. This work presents the design of an Acoustic Low Energy (ALE) beacon based on the emission of inaudible Linear Frequency Modulated (LFM) signals. This coding scheme provides high robustness to in-band noise, thus ensuring a reliable detection of the beacon at a practical range, after pulse compression. A series of experimental tests have been carried out with nine different Android devices to study the system performance. These tests have shown that the ALE beacon can be detected at one meter distance with signal-to-noise ratios as low as −12 dB. The tests have also demonstrated a detection rate above 80% for reception angles up to 50∘ with respect to the beacon’s acoustic axis at the same distance. Finally, a study of the ALE beacon energy consumption has been conducted demonstrating comparable power consumption to commercial Bluetooth Low Energy (BLE) beacons. Besides, the ALE beacon search can save up to 9% more battery of the Android devices than the BLE beacon scanning.

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