Enhancing Indoor Position Estimation Accuracy: Integration of Accelerometer, Raw Distance Data, and Extended Kalman Filter in Comparison to Vicon Motion Capture Data
Indoor positioning systems are a significant area of research and development, helping people navigate within buildings where GPS signals are unavailable. These systems have diverse applications, including aiding navigation in places like shopping malls, airports, and hospitals and improving emergen...
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MDPI AG
2023-11-01
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| author | Tolga Bodrumlu Fikret Çalışkan |
| author_facet | Tolga Bodrumlu Fikret Çalışkan |
| author_sort | Tolga Bodrumlu |
| collection | DOAJ |
| description | Indoor positioning systems are a significant area of research and development, helping people navigate within buildings where GPS signals are unavailable. These systems have diverse applications, including aiding navigation in places like shopping malls, airports, and hospitals and improving emergency evacuation processes. The purpose of this study is to evaluate various technologies and algorithms used in indoor positioning. This study focuses on using raw distance data and Kalman filters to enhance indoor position accuracy. It employs a trilateration algorithm based on Recursive Least Squares (RLS) for initial position estimation and combines the results with accelerometer data. The designed algorithm using real sensor data collected in an ROS(Robot Operating System) environment was tested, and the results obtained were compared with data obtained from the Vicon Indoor Positioning System. In this comparison, the Root Mean Square Error metric was used. As a result of the comparison, it was observed that the error obtained from the designed algorithm is less than that of the Vicon system. |
| format | Article |
| id | doaj-art-daaf6d725fe247acb12db4a52b24fa4f |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-daaf6d725fe247acb12db4a52b24fa4f2025-08-20T01:55:26ZengMDPI AGEngineering Proceedings2673-45912023-11-015814010.3390/ecsa-10-16089Enhancing Indoor Position Estimation Accuracy: Integration of Accelerometer, Raw Distance Data, and Extended Kalman Filter in Comparison to Vicon Motion Capture DataTolga Bodrumlu0Fikret Çalışkan1Mechatronics Engineering Department, Istanbul Technical University, Istanbul 34025, TurkeyControl and Automation Engineering Department, Istanbul Technical University, Istanbul 34025, TurkeyIndoor positioning systems are a significant area of research and development, helping people navigate within buildings where GPS signals are unavailable. These systems have diverse applications, including aiding navigation in places like shopping malls, airports, and hospitals and improving emergency evacuation processes. The purpose of this study is to evaluate various technologies and algorithms used in indoor positioning. This study focuses on using raw distance data and Kalman filters to enhance indoor position accuracy. It employs a trilateration algorithm based on Recursive Least Squares (RLS) for initial position estimation and combines the results with accelerometer data. The designed algorithm using real sensor data collected in an ROS(Robot Operating System) environment was tested, and the results obtained were compared with data obtained from the Vicon Indoor Positioning System. In this comparison, the Root Mean Square Error metric was used. As a result of the comparison, it was observed that the error obtained from the designed algorithm is less than that of the Vicon system.https://www.mdpi.com/2673-4591/58/1/40indoor positioningextended Kalman filterROSsensor fusion |
| spellingShingle | Tolga Bodrumlu Fikret Çalışkan Enhancing Indoor Position Estimation Accuracy: Integration of Accelerometer, Raw Distance Data, and Extended Kalman Filter in Comparison to Vicon Motion Capture Data Engineering Proceedings indoor positioning extended Kalman filter ROS sensor fusion |
| title | Enhancing Indoor Position Estimation Accuracy: Integration of Accelerometer, Raw Distance Data, and Extended Kalman Filter in Comparison to Vicon Motion Capture Data |
| title_full | Enhancing Indoor Position Estimation Accuracy: Integration of Accelerometer, Raw Distance Data, and Extended Kalman Filter in Comparison to Vicon Motion Capture Data |
| title_fullStr | Enhancing Indoor Position Estimation Accuracy: Integration of Accelerometer, Raw Distance Data, and Extended Kalman Filter in Comparison to Vicon Motion Capture Data |
| title_full_unstemmed | Enhancing Indoor Position Estimation Accuracy: Integration of Accelerometer, Raw Distance Data, and Extended Kalman Filter in Comparison to Vicon Motion Capture Data |
| title_short | Enhancing Indoor Position Estimation Accuracy: Integration of Accelerometer, Raw Distance Data, and Extended Kalman Filter in Comparison to Vicon Motion Capture Data |
| title_sort | enhancing indoor position estimation accuracy integration of accelerometer raw distance data and extended kalman filter in comparison to vicon motion capture data |
| topic | indoor positioning extended Kalman filter ROS sensor fusion |
| url | https://www.mdpi.com/2673-4591/58/1/40 |
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