Abstract
The location of objects is a growing research topic due, for instance, to the expansion of civil drones or intelligent vehicles. This expansion was made possible through the development of microelectromechanical systems (MEMS), inexpensive and miniaturized inertial sensors. In this context, this article describes the development of a new simulator which generates sensor measurements, giving a specific input trajectory. This will allow the comparison of pose estimation algorithms. To develop this simulator, the measurement equations of every type of sensor have to be analytically determined. To achieve this objective, classical kinematic equations are used for the more common sensors, i.e., accelerometers and rate gyroscopes. As nowadays, the MEMS inertial measurement units (IMUs) are generally magnetometer-augmented, an absolute world magnetic model is implemented. After the determination of the perfect measurement (through the error-free sensor models), realistic error models are developed to simulate real IMU behavior. Finally, the developed simulator is subjected to different validation tests.
Highlights
The dynamic location of objects is a predominant research topic in many fields.One of the main techniques for precise object location is inertial navigation.Inertial sensors exploit proprioceptive measurements, which result from the evolution of the object’s position
This paper presents the modeling of a magnetometer-augmented inertial measurement units (IMUs)
Different characteristics are taken into account: the multiple reference frames required to deal with short- and long-distance trajectories; the location of the IMU, which can be different from the center of the object; the modeling of the accelerometers, rate gyroscopes and magnetometers, which takes into account several sensor imperfections
Summary
The dynamic location of objects is a predominant research topic in many fields (robotics [1], intelligent vehicles [2], UAVs [3], bio-logging [4], etc.). As this domain is expanding, some applications require the sensors to withstand high dynamic loads; others are just embedded in our pockets; smartphones, for example, use MEMS technology for some navigation purposes In the latter case, magnetometers are added to measure the magnetic field and to determine the heading of the phone. Different characteristics are taken into account: the multiple reference frames required to deal with short- and long-distance trajectories (inertial frame, body frame, etc.); the location of the IMU, which can be different from the center of the object; the modeling of the accelerometers, rate gyroscopes and magnetometers, which takes into account several sensor imperfections (bias, noise, etc.) This tool is designed to provide sensor measurements, from an input trajectory defined by the object orientation quaternion and its spatial position (see Figure 1).
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