Abstract
Contemporary smartphones contain embedded inertial measurement unit (IMU), global navigation satellite system (GNSS), camera, and other sensors that are capable of providing user position, velocity, and attitude. However, the actual navigation performance capabilities of smartphones are difficult to use because of the low-cost and disparate sensors employed, differing software technologies adopted by manufacturers, and considerable influence of environmental conditions. In this study, we proposed multifusion schemes that integrated sensor data from smartphone IMU, GNSS chipsets, cameras, and ground control points (GCPs), using an extended Kalman filter to enhance the system navigation performance. Different processes of scale recovery and refreshed-simultaneous localization and mapping (SLAM) based on GNSS and GCPs corresponding to outdoor and indoor environments were proposed to increase the accuracy and robustness of the integrated system. To verify the performance of the integrated system, field test data were collected in an urban area of Tainan City, Taiwan. The experimental results indicated improvements of 87.09% and 36.27% for the refreshed-SLAM and its integration system, respectively, compared with visual-SLAM one-scale recovery and conventional integrated schemes. The proposed integrated system that uses smartphone sensor data increased navigation accuracy in GNSS-challenged environments.
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