Indoor 3D NLOS VLP using a binocular camera and a single LED
In this paper, we propose a non-line of sight (NLOS) visible light positioning (VLP) system using a binocular camera and a single light emitting diode (LED) for the realization of 3D positioning of an arbitrary posture. The proposed system overcomes the challenges of the shadowing/blocking of the line of sight (LOS) transmission paths between transmitters and receivers (Rxs) and the need for a sufficient number of LEDs that can be captured within the limited field of view of the camera-based Rx. We have developed an experimental testbed to evaluate the performance of the proposed system with results showing that the lowest average error and the root mean square error (RMSE) are 26.10 and 31.02 cm following an error compensation algorithm. In addition, a label-based enhanced VLP scheme is proposed for the first time, which has a great improvement on the system performance with the average error and RMSE values of 7.31 and 7.74 cm and a 90th percentile accuracies of < 11 cm.Get more news about https://www.as-video.com binocular camera module manufacturer,you can vist our website!
Location-based services as one of the most critical items required in intelligent and context-aware Internet-of-thing (IoT) systems are becoming increasingly important especially in indoor environments [1], where the global positioning system (GPS) does not work well since the radio frequency (RF) signals are easily obstructed [2]. To provide enhanced indoor location-based services, several positioning technologies based on different wireless signals have been proposed including wireless local area network (WLAN) [3], RF identification (RFID) [4], Bluetooth [5], ultra-wideband (UWB) [6], and visible light [7–11]. WLAN, RFID, and Bluetooth-based positioning systems have lower accuracy and must create and maintain the RF map frequently [12]. Whereas UWB-based positioning systems have higher accuracy but are costly [6]. Compared with the RF-based positioning technologies, the visible light positioning (VLP) system offers great potential because of its immunity to RF-induced electromagnetic interference, free and unrestricted spectrum, and a much higher level of security [13,14]. VLP uses light-emitting diodes (LED) lights and image sensors or photodiodes (PD) as the transmitters (Tx) and the receivers (Rx), respectively, to detect the signals and to estimate the relationships between the Txs and the Rxs. Considering the widespread use of LEDs-based lights in buildings and COMS cameras for monitoring and in smart devices we are seeing more research works in both VLP systems and the development of computer vision algorithms [15,16].
2. System model
The proposed NLOS VLP system is composed of a NLOS optical camera communication (OCC) subsystem for obtaining the received signal and a binocular stereo vision (BSV) model for transforming the coordinate of a single point from the pixel coordinate system (PCS) into CCS. In this section, first, we present an OCC signal recovery model followed by a BSV model. Next, we present an algorithm to estimate the position of the camera and an error compensation algorithm to optimize its performance. The schematic block diagram of the signal recovery process in OCC is depicted in Fig. 1.
3. Proposed system
The proposed system block diagram is depicted in Fig. 5. It is composed of two mains of NLOS OCC and BCam-based position estimation subsystems. Note, the signal acquisition at the Rx in the NLOS OCC subsystem is the main feature of the proposed system, which can also be realized in the OCC signal recovery model [26]. As for the position estimation subsystem, we have proposed two different schemes. The first estimates the camera position through the BSV model and a BPS algorithm by considering the symmetrical point of LED on the ground as the point 𝑃. Note that, the NLOS VLP works on the floor of an ordinary room. However, the system performance decreases as the ground roughness increases. What is worse, when the roughness is too large, the highlight area may not exist (cannot be detected) on the picture and the NLOS VLP may not work. In order to overcome this problem, we have proposed a LBE VLP scheme, where the point 𝑃 in the BSV model is replaced with a label, which is the orthographic projection of the LED on the reflecting surface and needs be marked in advance. Compared with the NLOS VLP detecting the highlight area by the pixel gray value, the LBE VLP identifies the label by its shape, so that rough reflection has less influence on it.
In this paper, we propose a non-line of sight (NLOS) visible light positioning (VLP) system using a binocular camera and a single light emitting diode (LED) for the realization of 3D positioning of an arbitrary posture. The proposed system overcomes the challenges of the shadowing/blocking of the line of sight (LOS) transmission paths between transmitters and receivers (Rxs) and the need for a sufficient number of LEDs that can be captured within the limited field of view of the camera-based Rx. We have developed an experimental testbed to evaluate the performance of the proposed system with results showing that the lowest average error and the root mean square error (RMSE) are 26.10 and 31.02 cm following an error compensation algorithm. In addition, a label-based enhanced VLP scheme is proposed for the first time, which has a great improvement on the system performance with the average error and RMSE values of 7.31 and 7.74 cm and a 90th percentile accuracies of < 11 cm.Get more news about https://www.as-video.com binocular camera module manufacturer,you can vist our website!
Location-based services as one of the most critical items required in intelligent and context-aware Internet-of-thing (IoT) systems are becoming increasingly important especially in indoor environments [1], where the global positioning system (GPS) does not work well since the radio frequency (RF) signals are easily obstructed [2]. To provide enhanced indoor location-based services, several positioning technologies based on different wireless signals have been proposed including wireless local area network (WLAN) [3], RF identification (RFID) [4], Bluetooth [5], ultra-wideband (UWB) [6], and visible light [7–11]. WLAN, RFID, and Bluetooth-based positioning systems have lower accuracy and must create and maintain the RF map frequently [12]. Whereas UWB-based positioning systems have higher accuracy but are costly [6]. Compared with the RF-based positioning technologies, the visible light positioning (VLP) system offers great potential because of its immunity to RF-induced electromagnetic interference, free and unrestricted spectrum, and a much higher level of security [13,14]. VLP uses light-emitting diodes (LED) lights and image sensors or photodiodes (PD) as the transmitters (Tx) and the receivers (Rx), respectively, to detect the signals and to estimate the relationships between the Txs and the Rxs. Considering the widespread use of LEDs-based lights in buildings and COMS cameras for monitoring and in smart devices we are seeing more research works in both VLP systems and the development of computer vision algorithms [15,16].
2. System model
The proposed NLOS VLP system is composed of a NLOS optical camera communication (OCC) subsystem for obtaining the received signal and a binocular stereo vision (BSV) model for transforming the coordinate of a single point from the pixel coordinate system (PCS) into CCS. In this section, first, we present an OCC signal recovery model followed by a BSV model. Next, we present an algorithm to estimate the position of the camera and an error compensation algorithm to optimize its performance. The schematic block diagram of the signal recovery process in OCC is depicted in Fig. 1.
3. Proposed system
The proposed system block diagram is depicted in Fig. 5. It is composed of two mains of NLOS OCC and BCam-based position estimation subsystems. Note, the signal acquisition at the Rx in the NLOS OCC subsystem is the main feature of the proposed system, which can also be realized in the OCC signal recovery model [26]. As for the position estimation subsystem, we have proposed two different schemes. The first estimates the camera position through the BSV model and a BPS algorithm by considering the symmetrical point of LED on the ground as the point 𝑃. Note that, the NLOS VLP works on the floor of an ordinary room. However, the system performance decreases as the ground roughness increases. What is worse, when the roughness is too large, the highlight area may not exist (cannot be detected) on the picture and the NLOS VLP may not work. In order to overcome this problem, we have proposed a LBE VLP scheme, where the point 𝑃 in the BSV model is replaced with a label, which is the orthographic projection of the LED on the reflecting surface and needs be marked in advance. Compared with the NLOS VLP detecting the highlight area by the pixel gray value, the LBE VLP identifies the label by its shape, so that rough reflection has less influence on it.
Indoor 3D NLOS VLP using a binocular camera and a single LED
In this paper, we propose a non-line of sight (NLOS) visible light positioning (VLP) system using a binocular camera and a single light emitting diode (LED) for the realization of 3D positioning of an arbitrary posture. The proposed system overcomes the challenges of the shadowing/blocking of the line of sight (LOS) transmission paths between transmitters and receivers (Rxs) and the need for a sufficient number of LEDs that can be captured within the limited field of view of the camera-based Rx. We have developed an experimental testbed to evaluate the performance of the proposed system with results showing that the lowest average error and the root mean square error (RMSE) are 26.10 and 31.02 cm following an error compensation algorithm. In addition, a label-based enhanced VLP scheme is proposed for the first time, which has a great improvement on the system performance with the average error and RMSE values of 7.31 and 7.74 cm and a 90th percentile accuracies of < 11 cm.Get more news about https://www.as-video.com binocular camera module manufacturer,you can vist our website!
Location-based services as one of the most critical items required in intelligent and context-aware Internet-of-thing (IoT) systems are becoming increasingly important especially in indoor environments [1], where the global positioning system (GPS) does not work well since the radio frequency (RF) signals are easily obstructed [2]. To provide enhanced indoor location-based services, several positioning technologies based on different wireless signals have been proposed including wireless local area network (WLAN) [3], RF identification (RFID) [4], Bluetooth [5], ultra-wideband (UWB) [6], and visible light [7–11]. WLAN, RFID, and Bluetooth-based positioning systems have lower accuracy and must create and maintain the RF map frequently [12]. Whereas UWB-based positioning systems have higher accuracy but are costly [6]. Compared with the RF-based positioning technologies, the visible light positioning (VLP) system offers great potential because of its immunity to RF-induced electromagnetic interference, free and unrestricted spectrum, and a much higher level of security [13,14]. VLP uses light-emitting diodes (LED) lights and image sensors or photodiodes (PD) as the transmitters (Tx) and the receivers (Rx), respectively, to detect the signals and to estimate the relationships between the Txs and the Rxs. Considering the widespread use of LEDs-based lights in buildings and COMS cameras for monitoring and in smart devices we are seeing more research works in both VLP systems and the development of computer vision algorithms [15,16].
2. System model
The proposed NLOS VLP system is composed of a NLOS optical camera communication (OCC) subsystem for obtaining the received signal and a binocular stereo vision (BSV) model for transforming the coordinate of a single point from the pixel coordinate system (PCS) into CCS. In this section, first, we present an OCC signal recovery model followed by a BSV model. Next, we present an algorithm to estimate the position of the camera and an error compensation algorithm to optimize its performance. The schematic block diagram of the signal recovery process in OCC is depicted in Fig. 1.
3. Proposed system
The proposed system block diagram is depicted in Fig. 5. It is composed of two mains of NLOS OCC and BCam-based position estimation subsystems. Note, the signal acquisition at the Rx in the NLOS OCC subsystem is the main feature of the proposed system, which can also be realized in the OCC signal recovery model [26]. As for the position estimation subsystem, we have proposed two different schemes. The first estimates the camera position through the BSV model and a BPS algorithm by considering the symmetrical point of LED on the ground as the point 𝑃. Note that, the NLOS VLP works on the floor of an ordinary room. However, the system performance decreases as the ground roughness increases. What is worse, when the roughness is too large, the highlight area may not exist (cannot be detected) on the picture and the NLOS VLP may not work. In order to overcome this problem, we have proposed a LBE VLP scheme, where the point 𝑃 in the BSV model is replaced with a label, which is the orthographic projection of the LED on the reflecting surface and needs be marked in advance. Compared with the NLOS VLP detecting the highlight area by the pixel gray value, the LBE VLP identifies the label by its shape, so that rough reflection has less influence on it.
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