Visible light positioning (VLP) using complementary metal–oxide–semiconductor (CMOS) image sensors is a cost-effective solution to the increasing demand for an indoor positioning system. However, in most of the existing VLP systems with an image sensor, researchers assume that the receiving image sensor is positioned parallel to the indoor floor without any tilting and, thus, have only focused on the high-precision positioning algorithm and ignored the proper light-emitting diode (LED)-ID recognition. To address these limitations, we present, herein, a smartphone CMOS image sensor and visible light-based indoor localization system for a receiver device in a tilted position, and we have applied a machine learning approach for optimized LED-ID detection. For detection of the LED-ID, we generated different features for different LED-IDs and utilize a machine learning method to identify each ID as opposed to using the conventional coding and decoding method. An image processing method was used for the image features extraction and selection. We utilized the rolling shutter mechanism of the smartphone CMOS image sensor in our indoor positioning system. Additionally, to improve the LED-ID detection and positioning accuracy with the tilting of the receiver, we utilized the embedded fusion sensors of the smartphone (e.g., accelerometer, gyroscope, and magnetometer, which can be used to extract the yaw, pitch, and roll angles). The experimental results for the proposed positioning system show that it can provide 2.49, 4.63, 8.46, and 12.20 cm accuracy with angles of 0, 5, 10, and 15°, respectively, within a 2 m × 2 m × 2 m positioning area.
Light positioning: A high-accuracy visible light indoor positioning system based on attitude identification and propagation model