Abstract
A ubiquitous sensor in embedded systems is the accelerometer, as it enables a range of applications. However, accelerometers experience nonlinearities in their outputs caused by error terms and axes misalignment. These errors are a major concern because, in applications such as navigations systems, they accumulate over time, degrading the position accuracy. Through a calibration procedure, the errors can be modeled and compensated. Many methods have been proposed; however, they require sophisticated equipment available only in laboratories, which makes them complex and expensive. In this article, a simple, practical, and low-cost calibration method is proposed. It uses a 3D printed polyhedron, benefiting from the popularisation and low-cost of 3D printing in the present day. Additionally, each polyhedron could hold as much as 14 sensors, which can be calibrated simultaneously. The method was performed with a low-cost sensor and it significantly reduced the root-mean-square error (RMSE) of the sensor output. The RMSE was compared with the reported in similar proposals, and our method resulted in higher performance. The proposal enables accelerometer calibration at low-cost, and anywhere and anytime, not only by experts in laboratories. Compensating the sensor’s inherent errors thus increases the accuracy of its output.
Highlights
In the Embedded world, a sensor that has reached a ubiquitous status is the accelerometer.Its small size, low-cost, and low power-consumption allow its integration in many consumer products, such as smart phones, health monitors, fitness trackers, gaming systems, etc
Due to the characteristics of the materials used in mechanical systems (MEMS) and their fabrication process, inertial measurement unit (IMU) present errors that affect the accuracy of the acceleration measurements [9]
The reason why we focus on these two noise terms is because they are a high order integration error [11], and they must be compensated when the sensor is used in applications that require high accuracy, for example, on Inertial Navigation Systems
Summary
In the Embedded world, a sensor that has reached a ubiquitous status is the accelerometer.Its small size, low-cost, and low power-consumption allow its integration in many consumer products, such as smart phones, health monitors, fitness trackers, gaming systems, etc. An inertial measurement unit (IMU) uses accelerometers to detect the linear acceleration of a body. An accelerometer consists of three orthogonally-mounted sensors that detect the acceleration in three axes: x, y, and z. Due to the characteristics of the materials used in MEMS and their fabrication process, IMUs present errors that affect the accuracy of the acceleration measurements [9]. Scale factor is the sensibility of the sensor, which corresponds to the ratio of input–output changes. It is evaluated as the gradient of the best straight line fitted by the least squares method to the input versus output data. Axes misalignment is a mounting error in the fabrication process that results in non-orthogonal axes in the sensor body
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