Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera
Smartphone camera or inertial measurement unit (IMU) sensor-based systems can be independently used to provide accurate indoor positioning results. However, the accuracy of an IMU-based localization system depends on the magnitude of sensor errors that are caused by external electromagnetic noise or...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/23/5084 |
| _version_ | 1817987319228006400 |
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| author | Alwin Poulose Dong Seog Han |
| author_facet | Alwin Poulose Dong Seog Han |
| author_sort | Alwin Poulose |
| collection | DOAJ |
| description | Smartphone camera or inertial measurement unit (IMU) sensor-based systems can be independently used to provide accurate indoor positioning results. However, the accuracy of an IMU-based localization system depends on the magnitude of sensor errors that are caused by external electromagnetic noise or sensor drifts. Smartphone camera based positioning systems depend on the experimental floor map and the camera poses. The challenge in smartphone camera-based localization is that accuracy depends on the rapidness of changes in the user’s direction. In order to minimize the positioning errors in both the smartphone camera and IMU-based localization systems, we propose hybrid systems that combine both the camera-based and IMU sensor-based approaches for indoor localization. In this paper, an indoor experiment scenario is designed to analyse the performance of the IMU-based localization system, smartphone camera-based localization system and the proposed hybrid indoor localization system. The experiment results demonstrate the effectiveness of the proposed hybrid system and the results show that the proposed hybrid system exhibits significant position accuracy when compared to the IMU and smartphone camera-based localization systems. The performance of the proposed hybrid system is analysed in terms of average localization error and probability distributions of localization errors. The experiment results show that the proposed oriented fast rotated binary robust independent elementary features (BRIEF)-simultaneous localization and mapping (ORB-SLAM) with the IMU sensor hybrid system shows a mean localization error of 0.1398 m and the proposed simultaneous localization and mapping by fusion of keypoints and squared planar markers (UcoSLAM) with IMU sensor-based hybrid system has a 0.0690 m mean localization error and are compared with the individual localization systems in terms of mean error, maximum error, minimum error and standard deviation of error. |
| first_indexed | 2024-04-14T00:20:24Z |
| format | Article |
| id | doaj.art-d93ee291fdda4a76a87a13591ac76c54 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T00:20:24Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d93ee291fdda4a76a87a13591ac76c542022-12-22T02:22:59ZengMDPI AGSensors1424-82202019-11-011923508410.3390/s19235084s19235084Hybrid Indoor Localization Using IMU Sensors and Smartphone CameraAlwin Poulose0Dong Seog Han1School of Electronics Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, KoreaSchool of Electronics Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, KoreaSmartphone camera or inertial measurement unit (IMU) sensor-based systems can be independently used to provide accurate indoor positioning results. However, the accuracy of an IMU-based localization system depends on the magnitude of sensor errors that are caused by external electromagnetic noise or sensor drifts. Smartphone camera based positioning systems depend on the experimental floor map and the camera poses. The challenge in smartphone camera-based localization is that accuracy depends on the rapidness of changes in the user’s direction. In order to minimize the positioning errors in both the smartphone camera and IMU-based localization systems, we propose hybrid systems that combine both the camera-based and IMU sensor-based approaches for indoor localization. In this paper, an indoor experiment scenario is designed to analyse the performance of the IMU-based localization system, smartphone camera-based localization system and the proposed hybrid indoor localization system. The experiment results demonstrate the effectiveness of the proposed hybrid system and the results show that the proposed hybrid system exhibits significant position accuracy when compared to the IMU and smartphone camera-based localization systems. The performance of the proposed hybrid system is analysed in terms of average localization error and probability distributions of localization errors. The experiment results show that the proposed oriented fast rotated binary robust independent elementary features (BRIEF)-simultaneous localization and mapping (ORB-SLAM) with the IMU sensor hybrid system shows a mean localization error of 0.1398 m and the proposed simultaneous localization and mapping by fusion of keypoints and squared planar markers (UcoSLAM) with IMU sensor-based hybrid system has a 0.0690 m mean localization error and are compared with the individual localization systems in terms of mean error, maximum error, minimum error and standard deviation of error.https://www.mdpi.com/1424-8220/19/23/5084indoor positioning system (ips)pedestrian dead reckoning (pdr)heading estimationindoor navigationimu sensorssmartphone camerakalman filtersensor fusionsimultaneous localization and mapping (slam)aruco markers |
| spellingShingle | Alwin Poulose Dong Seog Han Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera Sensors indoor positioning system (ips) pedestrian dead reckoning (pdr) heading estimation indoor navigation imu sensors smartphone camera kalman filter sensor fusion simultaneous localization and mapping (slam) aruco markers |
| title | Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera |
| title_full | Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera |
| title_fullStr | Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera |
| title_full_unstemmed | Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera |
| title_short | Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera |
| title_sort | hybrid indoor localization using imu sensors and smartphone camera |
| topic | indoor positioning system (ips) pedestrian dead reckoning (pdr) heading estimation indoor navigation imu sensors smartphone camera kalman filter sensor fusion simultaneous localization and mapping (slam) aruco markers |
| url | https://www.mdpi.com/1424-8220/19/23/5084 |
| work_keys_str_mv | AT alwinpoulose hybridindoorlocalizationusingimusensorsandsmartphonecamera AT dongseoghan hybridindoorlocalizationusingimusensorsandsmartphonecamera |