Abstract
Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF) algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV) for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV, respectively, compared to the reference path.
Highlights
Autonomous indoor navigation is increasingly popular for pedestrians, mobile robots, and unmanned aerial vehicles (UAVs)
The quaternion-based unscented Kalman filter (UKF) algorithm has the following advantages over the general Kalman filter and extended Kalman filter (EKF) [27,28,29,30,31,32,33,34,35,36,42,43,44,45,46]: (1) The quaternion-based orientation of an object has no singularities when the pitch angle passes through ± /2 than Euler angles or Direction Cosine Matrix (DCM); (2) Quaternion-based matrix transformation has higher computational efficiency than Euler angles and DCM, and it is more suitable for our low cost micro-electro-mechanical system (MEMS) multi-sensor system; (3) EKF models are linearized through a first order Taylor series expansion of the process/measurement models around the current state estimate, Jacobian matrix computation is quite complex; but UKF is second order approximation, which has capability of dealing with large and small attitude errors
In order to verify that our wearable multi-sensor system using the quaternion-based UKF algorithm can meet the accurate heading estimation needs of robots, pedestrians and UAVs in indoor environments, we carried out two experiments in our college building corridor
Summary
Autonomous indoor navigation is increasingly popular for pedestrians, mobile robots, and unmanned aerial vehicles (UAVs). Proposed to use a Kalman filter algorithm of fusing low-cost MEMS gyroscope and magnetometer in a smartphone to obtain heading estimation with quaternion mechanization [30]. To meet the requirements of accurate indoor heading estimation using a low-cost MEMS strapdown inertial navigation system, an effective method of quaternion-based UKF algorithm has been proposed. (2) Quaternion-based matrix transformation has higher computational efficiency than Euler angles and DCM, and it is more suitable for our low cost MEMS multi-sensor system; (3) EKF models are linearized through a first order Taylor series expansion of the process/measurement models around the current state estimate, Jacobian matrix computation is quite complex; but UKF is second order approximation, which has capability of dealing with large and small attitude errors.
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