![]() The receiver recognizes the light of the transmitters and then calculates its relative position to the transmitters. Among them, visible light positioning (VLP), using a transmitter and receiver that function via LED and camera, has shown some promise for indoor positioning ( Lausnay et al., 2016). #Y.f. zhang olympic games tokyo 2020 BluetoothThese include radio waves using Wi-Fi ( Chen et al., 2021), Bluetooth low energy (BLE) ( Faragher and Harle, 2015) or radio-frequency identification (RFID) ( Cillis et al., 2020), infrared (IR) using IR LEDs ( Hauschildt and Kirchhof, 2010), sound using speakers ( Murakami et al., 2021), computer vision using a camera ( Mautz and Tilch, 2011 Bai et al., 2019), visible lights ( Zhuang et al., 2018), and other methods ( Davidson and Piché, 2016). In our work, we focus on systems that satisfy a position estimation error of less than 1 m and an orientation estimation error of less than 10° as our initial target, and this seems acceptable in many indoor positioning and navigation situations.įrom this background, many methods for determining the indoor positioning of mobile and wearable devices have been proposed. These applications require sufficiently accurate estimations of position and orientation. The Japanese Ministry of Land, Infrastructure, Transport and Tourism launched the “Indoor High-precise Positioning Project” for the success of the Tokyo Olympic Games ( Ministry of Land and Tourism, 2018). The indoor positioning and navigation market is expected to grow 23.6 billion US dollars in 2023 ( IndustryArc, 2017). Furthermore, seamless navigation between indoor and outdoor environments is critical not only for visually impaired persons visiting unfamiliar places ( Duh et al., 2021) but also for people in emergency evacuation situations ( Meneguzzi et al., 2013). Indoor navigation is expected to have different applications compared with outdoor navigation: for example, assistance in a high-rise office building or purchase recommendation in a large-scale shopping mall. Indoor positioning is one of the key technologies for indoor navigation, with particular attention paid to methods that can be used with smartphones, which have become popular recently. Experimental results in a room measuring 4.0 m × 4.0 m using nine POIs each of which consists of 32 × 32 pixels in a captured image showed that the absolute errors at the 90th percentile for the 3-D coordinates were 0.2073 m, 0.1713 m, and 0.002464 m along the X, Y, and Z axes, respectively, and for the pitch, roll, and yaw angles were 5.78, 5.69 and 3.96 degrees, respectively. ![]() Several experiments to confirm the performance of RefRec+ were conducted. Using 2-D positions of multiple POIs and the angle of arrival method, RefRec+ obtains the 6DoF of the smartphone. RefRec+ estimates the 2-D position of a point of interest (POI) by calculating the received signal strength of individual light sources using the floor image captured by the camera. To overcome the problem of the limited field of views that causes failure to capture required numbers of light sources for positioning and that of the high computational complexity because of image processing to a large-sized pixel data, RefRec+ captures indirect lights from the light sources reflected via a floor. In most existing VLP systems, their front camera faces multiple light sources installed at different places on a ceiling to detect their direct signals. This paper describes a novel visible light positioning (VLP) system called RefRec+ allowing to estimate the six degree-of-freedom (6DoF) of a smartphone. 2Information Systems Architecture Science Research Division, National Institute of Informatics, Tokyo, Japan.1Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan.Masanori Sugimoto 1 *, Shota Shimada 1 and Hiromichi Hashizume 2 ![]()
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