40 years of sensor fusion for orientation tracking via magnetic and inertial measurement units: Methods, lessons learned, and future challenges
•Surveyed sensor fusion algorithms for orientation tracking with MIMUs.•Discussed fundamental algorithms like strap-down integration and vector observation.•Discussed advanced algorithms like complementary filter and Kalman filter.•Discussed modifications like gain tuning and imperfect measurement r...
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| Vydáno v: | Information fusion Ročník 68; s. 67 - 84 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.04.2021
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| Témata: | |
| ISSN: | 1566-2535 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | •Surveyed sensor fusion algorithms for orientation tracking with MIMUs.•Discussed fundamental algorithms like strap-down integration and vector observation.•Discussed advanced algorithms like complementary filter and Kalman filter.•Discussed modifications like gain tuning and imperfect measurement rejection.•Provided lessons learned from survey and future research challenges in the field.
Technological developments over the past two decades have resulted in the development of more accurate and lightweight low-cost magnetic and inertial measurement units (MIMUs). These developments have allowed the extensive application of MIMUs in various fields, specifically tracking the 3D orientation of a rigid body. Despite recent technological improvements, measurements from a tri-axial gyroscope, accelerometer, and/or magnetometer inside the MIMU are characterized by uncertainties. Numerous studies have been conducted to address these uncertainties and develop sensor fusion algorithms (SFAs) to estimate the 3D orientation accurately and robustly. This paper contributes to these efforts by providing a survey of the state-of-the-art SFAs for orientation estimation. We surveyed +250 publications, categorized the SFAs with various structures, identified the modifications proposed to improve their performance, and discussed the strengths and weaknesses of these approaches. We found that, while early SFAs were mostly a vector observation algorithm or an extended Kalman filter, to improve the computational efficiency, more recent works have developed SFAs with a complementary filter or complementary Kalman filter structure. At the same time, to improve the performance of the SFAs, several research teams have proposed various modifications to the basic structure of these filters, such as adaptive gain tuning or imperfect measurement rejection. We also provided an outlook on the lessons learned as well as the main challenges related to SFAs and discussed the practical steps toward developing an effective SFA. We have identified the need for benchmarking studies as the main challenge at the moment. This paper is among the first surveys which provide such breadth of coverage across different SFAs for tracking orientation with MIMUs.
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| ISSN: | 1566-2535 |
| DOI: | 10.1016/j.inffus.2020.10.018 |