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Abstract
Modern smartphones contain embedded global navigation satellite systems (GNSSs), inertial measurement units (IMUs), cameras, and other sensors which are capable of providing user position, velocity, and attitude. However, it is difficult to utilize the actual navigation performance capabilities of smartphones due to the low-cost and disparate sensors, software technologies adopted by manufacturers, and the significant influence of environmental conditions. In this study, we proposed a scheme that integrated sensor data from smartphone IMUs, GNSS chipsets, and cameras using an extended Kalman filter (EKF) to enhance the navigation performance. The visual data from the camera was preprocessed using oriented FAST (Features from accelerated segment test) and rotated BRIEF (Binary robust independent elementary features)-simultaneous localization and mapping (ORB-SLAM), rescaled by applying GNSS measurements, and converted to velocity data before being utilized to update the integration filter. In order to verify the performance of the integrated system, field test data was collected in a downtown area of Tainan City, Taiwan. Experimental results indicated that visual data contributed significantly to improving the accuracy of the navigation performance, demonstrating improvements of 43.0% and 51.3% in position and velocity, respectively. It was verified that the proposed integrated system, which used data from smartphone sensors, was efficient in terms of increasing navigation accuracy in GNSS-challenging environments.
Highlights
Navigation involves the determination of the time-varying position, velocity, and attitude of a moving body
global navigation satellite systems (GNSSs) is used to correct for the systematic errors associated with the inertial sensors, which are composed of biases, scale factors, and drifts, whereas the inertial navigation system (INS) is used as a bridge for seamless navigation when the GNSS experiences an outage [3,4]
The results showed that the system was able to maintain higher positioning accuracy during Global Positioning System (GPS) dropouts, this system utilized only the gyroscope among the many other available smartphone sensors for the strapdown algorithm
Summary
Navigation involves the determination of the time-varying position, velocity, and attitude of a moving body. Due to the increased accuracy and decreased cost of microelectromechanical sensors (MEMS) [2], smartphones have been outfitted with various embedded sensors, such as global navigation satellite systems (GNSSs), cameras, gyroscopes, accelerometers, and magnetometers. These improve the smartphone’s usefulness to navigation systems. The experimental results demonstrated the same level of estimation accuracy as state-of-the-art VINS algorithms, this study only involved testing the system within an indoor environment over a short travel distance. These data were fused together using an EKF estimation algorithm
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