Next Article in Journal
UAV Applications for Determination of Land Deformations Caused by Underground Mining
Next Article in Special Issue
On the Recursive Joint Position and Attitude Determination in Multi-Antenna GNSS Platforms
Previous Article in Journal
Risk Prediction of Coastal Hazards Induced by Typhoon: A Case Study in the Coastal Region of Shenzhen, China
Previous Article in Special Issue
Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas
Article

The Performance Analysis of INS/GNSS/V-SLAM Integration Scheme Using Smartphone Sensors for Land Vehicle Navigation Applications in GNSS-Challenging Environments

1
Department of Geomatics, National Cheng Kung University, Tainan 701, Taiwan
2
Department of Geomatics and Land-administration, Hanoi University of Mining and Geology, Hanoi 10000, Vietnam
3
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1732; https://doi.org/10.3390/rs12111732
Received: 23 April 2020 / Revised: 10 May 2020 / Accepted: 26 May 2020 / Published: 28 May 2020
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. View Full-Text
Keywords: INS; integration; smartphone; EKF; IMU; SLAM; GNSS-challenging environments INS; integration; smartphone; EKF; IMU; SLAM; GNSS-challenging environments
Show Figures

Figure 1

MDPI and ACS Style

Chiang, K.-W.; Le, D.T.; Duong, T.T.; Sun, R. The Performance Analysis of INS/GNSS/V-SLAM Integration Scheme Using Smartphone Sensors for Land Vehicle Navigation Applications in GNSS-Challenging Environments. Remote Sens. 2020, 12, 1732. https://doi.org/10.3390/rs12111732

AMA Style

Chiang K-W, Le DT, Duong TT, Sun R. The Performance Analysis of INS/GNSS/V-SLAM Integration Scheme Using Smartphone Sensors for Land Vehicle Navigation Applications in GNSS-Challenging Environments. Remote Sensing. 2020; 12(11):1732. https://doi.org/10.3390/rs12111732

Chicago/Turabian Style

Chiang, Kai-Wei, Dinh T. Le, Thanh T. Duong, and Rui Sun. 2020. "The Performance Analysis of INS/GNSS/V-SLAM Integration Scheme Using Smartphone Sensors for Land Vehicle Navigation Applications in GNSS-Challenging Environments" Remote Sensing 12, no. 11: 1732. https://doi.org/10.3390/rs12111732

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop