Research Article
A Novel Indoor Positioning Algorithm Based on IMU
@INPROCEEDINGS{10.1007/978-3-030-41117-6_14, author={Bi He and Hui Wang and Minshuo Li and Kozyrev Yury and Xu Shi}, title={A Novel Indoor Positioning Algorithm Based on IMU}, proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part II}, proceedings_a={CHINACOM PART 2}, year={2020}, month={2}, keywords={Inertial sensor Indoor positioning Accelerometer Gyroscope Zero-velocity update}, doi={10.1007/978-3-030-41117-6_14} }- Bi He
Hui Wang
Minshuo Li
Kozyrev Yury
Xu Shi
Year: 2020
A Novel Indoor Positioning Algorithm Based on IMU
CHINACOM PART 2
Springer
DOI: 10.1007/978-3-030-41117-6_14
Abstract
Although the Global Positioning System (GPS) can provide more accurate outdoor positioning services, it cannot detect the signals in indoor environments or in densely populated areas. Therefore, indoor positioning service has gradually been paid more attention. Most researchers currently use a nine-axis inertial sensor for indoor positioning. However, when the object is moving fast and frequently, it is obvious that using nine-axis inertial sensor has a large amount of computation. In addition, Kalman filtering algorithm is always cumbersome when data fusion is carried out for inertial sensors. The use of zero-velocity update algorithm (ZVU) to improve double integral can reduce the cumulative error, but the degree is far from enough. This paper mainly completes the following works: Firstly, the six-axis inertial sensor is used for indoor positioning. Then the digital motion processor is used instead of Kalman filter for attitude solution. Lastly, ZVU is optimized. Specifically, in the six-axis inertial sensor, the three-axis accelerometer is used to measure the force of the object, and the three-axis gyroscope is used to detect the current posture of the object. Since the three-axis magnetometer is missing, it is possible to effectively reduce a part of the calculation amount. In addition, the digital motion processor is used instead of the Kalman filter for the attitude solution, which avoids cumbersome filtering and data fusion. Finally, we optimize the ZVU so that the cumulative error is reduced again. The experimental results show that the algorithm proposed in this paper has certain feasibility and practical application value.
