An Automatic Site Survey Approach for Indoor Localization Using a Smartphone

Qing, Liang; Liu, Ming

doi:10.1109/tase.2019.2918030

Cited by 51 publications

(19 citation statements)

References 27 publications

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“…In order to obtain the transmitter locations and identities, we obtained the receiver position through SLAM, rather than the manual measurements used in [31,50]. Contrary to [35], we were also able to obtain light source identities. Yue et al [36] did use modulated LEDs, yet they have significantly lower accuracy compared to our work.…”

Section: Discussionmentioning

confidence: 99%

“…In this work, we will focus on the calibration of light source locations and identifiers without prior knowledge or additional positioning systems, of which there are few examples. Liang and Liu [35] crowdsourced the construction of a signal strength map of opportunistic signals. Similar to [14,16], user trajectories were obtained with the help of a modified graphSLAM algorithm.…”

Section: Positioning Technology Calibration Methods Calibrated Parametersmentioning

confidence: 99%

“…The entire space needs to be visited by the surveyor and as mentioned before, this process may have to be repeated. Manual site survey is time-consuming and labor-intensive and is thus not always practical in large indoor spaces [ 35 ]. The use of mobile robots has therefore been proposed to simplify this task.…”

Section: Related Workmentioning

confidence: 99%

“…Pedestrian Dead Reckoning (PDR) is often used to obtain a rough estimate of the user’s trajectory, and drift is corrected by using absolute location fixes (for example, from GNSS signals or near-field communication tags) [ 16 ]. Alternatively, other signals of opportunity such as Wi-Fi, magnetic field or even ambient light [ 35 ] can be used to compensate PDR drift. When using signalSLAM to calibrate fingerprinting IPS, the main goal is to reconstruct the trajectory of the user and to add the measured signal strengths to the map based on that trajectory.…”

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: Discussionmentioning

confidence: 99%

Section: Positioning Technology Calibration Methods Calibrated Parametersmentioning

confidence: 99%

Section: Related Workmentioning

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

View full text Add to dashboard Cite

show abstract

“…At present, various indoor positioning system have been proposed ,for example, Wi-Fi [2], Wlan [3]positioning system can satisfy the requirements of indoor positioning. However, these devices still have deficiencies in positioning.…”

Section: Introductionmentioning

confidence: 99%

Indoor High Precision Three-Dimensional Positioning System Based on Visible Light Communication Using Improved Hybrid Bat Algorithm

et al. 2020

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To solve problems of low positioning accuracy and long time-consuming in indoor visible light three-dimensional positioning system, a visible light three-dimensional positioning method based on the improved hybrid bat algorithm (IHBA) is proposed in this paper. Firstly, some beacon points are set at the beginning of the IHBA to reduce the number of iterations. Secondly, weight coefficient is defined to improve positioning accuracy when the fitness function is constructed. Thirdly, aiming at controlling the search speed reasonably, an adaptive search factor is introduced while the bat individual update formula is designed. Finally, the chaotic perturbation operation is used to avoid the algorithm falling into local optimum. In indoor simulation environment, the size of which is 5m×5m×3m, the average positioning error of the IHBA is 1.16cm and the positioning time is 1.85s. To verify the actual effectiveness of IHBA, a receiver is placed in the experimental environment, the size of which is 1.5m×1.2m×2m. Through the optical power detected by the receiver, the processor calculate its coordinates with IHBA. The average positioning error of the IHBA is 3.64cm and the positioning time is 0.89s. In this paper, the three-dimensional positioning system based on the IHBA algorithm can achieve high-precision and low-time positioning, and it has practical application value in existing indoor positioning methods.

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Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

Varma

Anand

2022

Peer-to-Peer Netw. Appl.

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IoT services are the basic building blocks of smart cities, and some of such crucial services are provided by smart buildings. Most of the services like smart meters, indoor navigation, lighting control, etc., which contribute to smart buildings, need the locations of people or objects within the building. This gave rise to Indoor Localization, where only the infrastructure of the building has to be used for localization as accessing the Global Positioning System is difficult in indoor environments. Many approaches have been proposed to predict locations based on the infrastructure available indoors, and some of such techniques use Wi-Fi access points. Still, unfortunately, very few studies have concentrated on tolerating faults while being cost-effective. This work discusses hardware implementation of indoor localization. It then proposes a learning algorithm SRNN (Speed Conscious Recurrent Neural Network) that uses the RSSI (Received Signal Strength Indicator) values of available Wi-Fi access points in the building and predicts the location. Also, fault-tolerant approaches termed nearest RSSI and the most recent RSSI using Kullback-Leibler Divergence have been proposed to improve the location accuracy when access points go down and are prone to faults. Both the proposed approaches nearest RSSI and most recent RSSI along with SRNN improve the location accuracy by 4% and 2.1%, respectively, over existing techniques and contribute to optimizing predicted location's accuracy in Indoor Localization an IoT service for smart buildings.

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An Automatic Site Survey Approach for Indoor Localization Using a Smartphone

Cited by 51 publications

References 27 publications

Calibration of Visible Light Positioning Systems with a Mobile Robot

Calibration of Visible Light Positioning Systems with a Mobile Robot

Indoor High Precision Three-Dimensional Positioning System Based on Visible Light Communication Using Improved Hybrid Bat Algorithm

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

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