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Different LED images captured by CMOS sensor, (a) LED1; (b) LED2; (c) LED3; (d) LED4; (e) LED5; (f) LED6.
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Complementary Metal-Oxide-Semiconductor (CMOS) sensor based visible light positioning (VLP) has been widely studied in recent years. In most researches about CMOS sensor based VLP, researchers focus on the high-precision positioning algorithm but ignore that the accuracy of LED-ID detection and recognition plays a more important role in a VLP syste...
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Context 1
... images of different LEDs modulated by PWM with different frequency and duty-ratio are shown in Fig. 3. it can be clearly seen that, Fig. 3(a)-(c) or (d) and (e) are modulated with the same frequency but different duty-ratio. At the same distance between LED and camera, the number of bright stripes are the same, and the duty-ratio of bright stripes are different. In Fig. 3(a) and (d), the modulated frequency are different, and the duty-ratio are the same, so we could observe that they have the same duty-ratio of bright stripes and different number of bright stripes. ...
Context 2
... images of different LEDs modulated by PWM with different frequency and duty-ratio are shown in Fig. 3. it can be clearly seen that, Fig. 3(a)-(c) or (d) and (e) are modulated with the same frequency but different duty-ratio. At the same distance between LED and camera, the number of bright stripes are the same, and the duty-ratio of bright stripes are different. In Fig. 3(a) and (d), the modulated frequency are different, and the duty-ratio are the same, so we could observe that they have the same duty-ratio of bright stripes and different number of bright stripes. ...
Context 3
... images of different LEDs modulated by PWM with different frequency and duty-ratio are shown in Fig. 3. it can be clearly seen that, Fig. 3(a)-(c) or (d) and (e) are modulated with the same frequency but different duty-ratio. At the same distance between LED and camera, the number of bright stripes are the same, and the duty-ratio of bright stripes are different. In Fig. 3(a) and (d), the modulated frequency are different, and the duty-ratio are the same, so we could observe that they have the same duty-ratio of bright stripes and different number of bright stripes. ...
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In recent years, visible light positioning (VLP) technology based on Complementary Metal Oxide Semiconductor (CMOS) sensors has been widely studied due to its high precision and high robustness. However, the existing VLP algorithm based on image sensors often fails to achieve a good positioning effect when the camera is tilted. In order to solve th...
Citations
... ph@tf.uni-kiel. de) to extract the LED ID and thus the corresponding position [7]. 'CELLI' is another approach that could also be attributed to proximity estimation [8]. ...
This paper extends previous work on liquid-crystal-display-based angle-of-arrival (AoA) estimation, initially performed for only a single light source (LS), by considering scenarios with multiple LSs. First, the system model is revisited to investigate parameters that influence detection of multiple LSs. Next, challenges such as separation of co-located LSs and ambiguities are investigated. It is identified that target separation gets increasingly challenging with decreasing distance between LSs, and AoA estimation developed for a single LS leads to ambiguities in scenarios with multiple LSs. These problems are solved by a novel angular separation algorithm and dedicated intensity measurements. Finally, two new experimental setups are introduced to investigate and model optical diffraction effects, and to verify theoretical derivations by measurement results.
... The VLP method based on imaging is simply referred to as the Image Sensor (IS) method. It mainly uses a CMOS sensor to image the VLP light source, utilizes the rolling shutter effect of the CMOS sensor to obtain the stripe image of the VLP light source, and obtains the coordinate information corresponding to the light source ID through the stripe image [68]. Then, the spatial coordinates of the photographic point relative to the VLC light source are calculated through the principle of photogrammetry, thereby achieving positioning [69]. ...
High-precision indoor positioning technology is regarded as one of the core components of artificial intelligence (AI) and Internet of Things (IoT) applications. Over the past decades, society has observed a burgeoning demand for indoor location-based services (iLBSs). Concurrently, ongoing technological innovations have been instrumental in establishing more accurate, particularly meter-level indoor positioning systems. In scenarios where the penetration of satellite signals indoors proves problematic, research efforts focused on high-precision intelligent indoor positioning technology have seen a substantial increase. Consequently, a stable assortment of location sources and their respective positioning methods have emerged, characterizing modern technological resilience. This academic composition serves to illuminate the current status of meter-level indoor positioning technologies. An in-depth overview is provided in this paper, segmenting these technologies into distinct types based on specific positioning principles such as geometric relationships, fingerprint matching, incremental estimation, and quantum navigation. The purpose and principles underlying each method are elucidated, followed by a rigorous examination and analysis of their respective technological strides. Subsequently, we encapsulate the unique attributes and strengths of high-precision indoor positioning technology in a concise summary. This thorough investigation aspires to be a catalyst in the progression and refinement of indoor positioning technologies. Lastly, we broach prospective trends, including diversification, intelligence, and popularization, and we speculate on a bright future ripe with opportunities for these technological innovations.
... RSS measurements can be susceptible to environmental factors such as signal attenuation, reflections and interference. However, with proper calibration and signal processing techniques, RSS-based VLP systems have shown promising results in terms of accuracy and costeffectiveness [70]. In [71], a RSS-based VLP system is shown with simulation results indicating a positioning accuracy in the low double-digit centimeter range. ...
Accurate indoor positioning is becoming increasingly important, especially in highly automated industrial environments with robots. In addition, LED-based lighting is also being used more and more frequently in such application fields. In the present work, the possibility to exploit the LED lighting infrastructure with a novel approach for implementing an accurate indoor positioning system is investigated. For this purpose, a demonstrator luminaire LEDPOS is proposed and evaluated that combines visible light sensing based on backscattered reflections to accurately estimate the two-dimensional position of a retroreflective foil at the floor while providing simultaneously an unimpaired room illumination. In particular, the same LED elements are shared for illumination and for the sensing functionality. Furthermore, the algorithm for data evaluation and position determination is based on a machine learning approach that is implemented on the edge in the luminaire. Thus, the presented approach allows for a simple and cost-efficient implementation in different applications. The experimental characterization of the LEDPOS demonstrator in a real-world scenario shows that a very good positioning accuracy can be achieved, in which the average error for the two-dimensional position of the retroreflective foil within an area of 0.64 m
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remains in the range of 3 cm.
... However, the angle-based and time-based positioning methods require additional synchronization equipment. RSSI only needs one single PD, hence lower hardware cost, which makes it the most widely used indoor visible light positioning method [16]. ...
In RSSI-based indoor visible light positioning systems, when only RSSI is used for trilateral positioning, the receiver height needs to be known to calculate distance. Meanwhile, the positioning accuracy is greatly affected by multi-path effect interference, with the influence of the multi-path effect varying across different areas of the room. If only one single processing is used for positioning, the positioning error in the edge area will increase sharply. In order to address these problems, this paper proposes a new positioning scheme, which uses artificial intelligence algorithms for point classification. Firstly, height estimation is performed according to the received power data structure from different LEDs, which effectively extends the traditional RSSI trilateral positioning from 2D to 3D. The location points in the room are then divided into three categories: ordinary points, edge points and blind points, and corresponding models are used to process different types of points, respectively, to reduce the influence of the multi-path effect. Next, processed received power data are used in the trilateral positioning method for calculating the location point coordinates, and to reduce the room edge corner positioning error, so as to reduce the indoor average positioning error. Finally, a complete system is built in an experimental simulation to verify the effectiveness of the proposed schemes, which are shown to achieve centimeter-level positioning accuracy.
... In terms of visible light positioning, two primary techniques have been developed: the camera-based and the photodetector (PD)based methods. The camera-based methods adopt additional array detectors to acquire the sensing information, whereas they are independent from the communication channel and are costly in computation budget [13,14]. The PD-based methods require an array of separately placed transmitters that encode unique identification signals and measure the receiver's response to multiple sources [15,16]. ...
Visible light communication plays an essential role in the next-generation 6G network due to its extremely high bandwidth and ultrafast transmission speed. Incorporating position sensing functionality into the communication system is highly desired, for achieving target-oriented beamforming and accommodating high-speed data service. However, a universal solution to integrated sensing and light communication remains challenging. Here, we demonstrate an integrated system that accomplishes concurrent high-accuracy sensing and high-speed data transmission by leveraging the spatio-temporal characteristics of the light field. Utilizing a compressive angular projection imaging scheme and jointly optimized waveform design, the integrated system allows approximately 3Gbps transmission and real-time three-dimensional localization from the user's perspective with 1 mm lateral resolution and 4 cm in depth within 0.6m×0.6m×0.6m volume over 2 m distance. This capability enables adaptive beamforming, which significantly enhances data rate by 122% (up to 6.18 Gbps), permitting errorless transmission of high-throughput virtual reality video. Furthermore, the system’s localization precision is improved by 2-fold laterally and 4-fold vertically using focused structured illumination. Our work offers a promising route for intelligent wireless light communication systems with perception faculty and high capacity, presenting the possibility of cable-free, immersive virtual reality experiences.
... For most of the VLP systems based on image sensors (ISs) [11][12][13][14], the IS needs to capture at least two LED lamps at the same time to establish the equations required to determine the receiver's position. But in practical applications, it is difficult to meet such positioning conditions. ...
A single-lamp visible light positioning (VLP) system based on an optical camera is proposed and demonstrated. The system uses a circular light-emitting diode (LED) to transmit ID data, and a common smartphone camera for positioning. First, we propose a scheme to calculate the azimuth angle using a marked lamp bead, avoiding the use of a magnetometer, which can improve the accuracy of the azimuth angle. Second, we propose a new positioning method using a chord in the image plane, which passes through the midpoints of two chords parallel to the LED plane. The experimental results show that when the experimental area is $1.8\;{\rm m} \times 3.6\;{\rm m} $ 1.8 m × 3.6 m , the three-dimensional (3D) average positioning errors are 8.0, 7.5, and 7.2 cm for the heights of 1.75, 1.45, and 1.2 m.
... In the literature, different VLIP schemes were proposed and demonstrated. One scheme is based on proximity [9]. It was realized by identifying the received optical identifier (ID) and lookup table for the positioning, and its accuracy depended on the illumination zone of each LED. ...
The high precision three-dimensional (3D) visible light-based indoor positioning (VLIP) systems have gained much attention recently for people or robot navigation, access tracking, etc. In this work, we put forward and present the first demonstration, up to the authors’ knowledge, of a 3D VLIP system utilizing a two-stage neural network (TSNN) model. The positioning performance would degrade when the distance between the light emitting diode (LED) plane and the receiver (Rx) plane increases; however, because of the finite LED field-of-view (FOV), light non-overlap zones are created. These light non-overlap zones will produce high positioning error particularly for the 3D VLIP systems. Here, we also propose and demonstrate the Received-Intensity-Selective-Enhancement scheme, known as RISE, to alleviate the light non-overlap zones in the VLIP system. In a practical test-room with dimensions of 200 × 150 × 300 cm3, the experimental results show that the mean errors in the training and testing data sets are reduced by 54.1% and 27.9% when using the TSNN model with RISE in the z-direction, and they are reduced by 39.1% and 37.8% in the xy-direction, respectively, when comparing that with using a one stage NN model only. At the cumulative distribution function (CDF) P90, the TSNN model with RISE can reduce the errors by 36.78% when compared with that in the one stage NN model.
... Thanks to the high refresh rates (≥120 Hz) in off-the-shelf electronic displays, it is possible to hide data in high-frame-rate videos to cheat the human eye, which has much lower perception rates (40-50 Hz). D2C technology is a type of visible light communications [3][4][5] that can be applied anywhere in the environment where there are electronic displays and camera sensors, mainly at short communication distances. ...
Display-to-camera (D2C) communications has emerged as a key method for next-generation videos that offer side information to camera-equipped devices during normal viewing. This paper presents Deep learning-driven Complementary Color Barcode-based Optical Camera Communications (DeepCCB-OCC), a D2C system using multiple deep neural networks built for imperceptible transmission and reliable communication in a D2C link. DeepCCB-OCC takes advantage of a the You Only Look Once (YOLO) model to provide seamless detection of a color barcode area in electronic displays. To identify transmitted color barcode symbols in the received image, we define various color barcode patterns caused by the synchronization jitter between the camera and the display. Then, DeepCCB-OCC incorporates convolutional neural network (CNN) models to accurately detect the pilot and data symbols in the transmission packets, regardless of the various D2C environments. Experiments with a commercial monitor and a smartphone demonstrate that DeepCCB-OCC outperforms the conventional CCB-OCC system from various distances and angles of a D2C link. The experiment results prove that, when the alignment angle was 20 degrees at a distance of 90 cm between the display and the camera, the proposed scheme achieved approximately 79.1 bps, which showed a performance improvement of 14.1% compared to the existing technique.
... Few researchers have begun to study the robustness problem of VLP systems, although it is more critical for the practicality and availability of VLP systems, especially for high-speed moving objects. Xie et al. proposed a proximity-based visible light localization LED-ID detection and recalibration method to improve the robustness of their system by using a machine learning approach (Fisher classifier or linear support vector machine) to increase the success rate of identifying the UID of VLP-LED lamps from the captured images [19]. However, their work only considers the low-speed scenarios where the captured images are not deteriorated by high-speed motion effects. ...
Robustness is a key factor for real-time positioning and navigation, especially for high-speed vehicles. While visible light positioning (VLP) based on LED illumination and image sensors is widely studied, most of the VLP systems still suffer from the high positioning latency and the image blurs caused by high-speed movements. In this paper, a robust VLP system for high-speed vehicles is proposed based on a deep learning and data-driven approach. The proposed system can significantly increase the success rate of decoding VLP-LED user identifications (UID) from blurred images and reduce the computational latency for detecting and extracting VLP-LED stripe image regions from captured images. Experimental results show that the success rate of UID decoding using the proposed BN-CNN model could be higher than 98% when that of the traditional Zbar-based decoder falls to 0, while the computational time for positioning is decreased to 9.19 ms and the supported moving speed of our scheme can achieve 38.5 km/h. Therefore, the proposed VLP system can enhance the robustness against high-speed movement and guarantee the real-time response for positioning and navigation for high-speed vehicles.
... Several indoor positioning scheme are proposed, including Bluetooth, Wireless Fidelity (WiFi) or hybrid VLP [14]. Proximity based VLP system [15] is simple; however, the positioning error is unsatisfactory for high precision applications. Time-of-arrival (TOA) [16] and time-difference-of-arrival (TDOA) [17] based VLC systems are also proposed; however, strict synchronization between the transmitter (Tx) and receiver (Rx) is required. ...
We put forward and transform the commercially available lighting design software into an indoor visible light positioning (VLP) design tool. The proposed scheme can work well with different deep learning methods for reducing the loading of training data set collection. The indoor VLP models under evaluation include second order regression, fully-connected neural-network (FC-NN), and convolutional neural-network (CNN). Experimental results show that the similar positioning accuracy can be obtained when the indoor VLP models are trained with experimentally acquired data set or trained with software obtained data set. Hence, the proposed method can reduce the training loading for the indoor VLP.
