Abstract
The problem of heading drift error using only low cost Micro-Electro-Mechanical (MEMS) Inertial-Measurement-Unit (IMU) has not been well solved. In this paper, a heading estimation method with real-time compensation based on Kalman filter has been proposed, abbreviated as KHD. For the KHD method, a unified heading error model is established for various predictable errors in magnetic compass for pedestrian navigation, and an effective method for solving the model parameters is proposed in the indoor environment with regular structure. In addition, error model parameters are solved by Kalman filtering algorithm with building geometry information in order to achieve real-time heading compensation. The experimental results show that the KHD method can not only effectively correct the original heading information, but also effectively inhibit the accumulation effect of positioning errors. The performance observed in a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirms that apply KHD method to PDR(Pedestrian Dead Reckoning) algorithm can reliably achieve meter-level positioning using a low cost MEMS IMU only.
Highlights
Heading estimation is one of the key problems in the algorithm of pedestrian dead reckoning, and the accuracy of orientation estimation generates direct impact on the results of position calculation
Error accumulation of heading is the inherent defect of any pedestrian dead reckoning (PDR) algorithm based on MEMS IMU, which can be reduced by high-precision IMU
Experiments show that the proposed method of calculating real-time heading compensation can achieve accurate error model parameters training in indoor environment with regular structure, here regular structure refers to a number of straight corridors in the indoor structure
Summary
Heading estimation is one of the key problems in the algorithm of pedestrian dead reckoning, and the accuracy of orientation estimation generates direct impact on the results of position calculation. Experiments show that the proposed method of calculating real-time heading compensation can achieve accurate error model parameters training in indoor environment with regular structure, here regular structure refers to a number of straight corridors in the indoor structure. The direction of walk in the corridor basically remains stable, so that the direction of walk can be subject to filtering constraint by using the known direction of a corridor This method effectively improves the original heading information, inhibits the accumulation effect of positioning error, and meets the needs of real-time pedestrian navigation.
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