#AutumnLifeCheck-in Season#Inertial sensors are one of the commonly used sensors in many devices such as unmanned vehicles, which provide attitude information and motion status to devices by measuring the acceleration and angular velocity of objects. However, inertial sensors often generate errors when measuring data, including offset errors, proportional errors, and background white noise. This article will explain the effects of these errors and how they can be corrected by calibration.
1. Influence and calibration of offset error
Offset error is a condition in which an inertial sensor outputs non-zero data even in the absence of rotation or acceleration. This error causes the inertial sensor to have an angular shift even in the absence of external forces. Once the output of the accelerometer is integrated twice to calculate the displacement data, even if the offset error is small, they will be amplified over time, ultimately making it impossible to accurately track the position of the unmanned vehicle.
In order to correct the offset error, we need to calibrate the inertial sensor. Calibration is divided into static calibration and dynamic calibration. Static calibration is a calibration that is performed without any movement, correcting the sensor to the zero position by recording its output offset value. Dynamic calibration is the process of calibrating a sensor when performing a specific action or movement. By performing actions or movements under known conditions and comparing the sensor outputs, the offset error of the sensor can be accurately calculated and corrected.
However, it is important to note that the error of an inertial sensor varies with temperature. As a result, even with calibration, errors can accumulate over time.
2. Influence and calibration of proportional error
Proportional error is when the ratio between the measured output and the change in the measured input is inconsistent. Similar to offset errors, proportional errors are amplified after two integrations, resulting in the accumulation of displacement errors.
In order to calibrate the proportional error, we need to determine the exact relationship between the sensor output and the input being measured, and correct the sensor output with the calibration parameters. Calibration can be performed by providing experimental data from known inputs and comparing them with the sensor output.
However, like offset errors, scale errors can vary with time and temperature, so calibration may not be a one-time solution.
3. Influence and correction of background white noise
Background white noise refers to the random noise in the output of the sensor, which, if not corrected, will also greatly affect the positioning accuracy of the unmanned vehicle. Background white noise is often caused by the characteristics of the sensor itself, such as noise from electronic components, interference from circuits, etc.
In order to correct background white noise, we can filter out the noise through signal processing methods. A common method is to use a digital filter to remove noise in a specific frequency range. At the same time, we can also use statistical methods to model the noise to more accurately estimate the true attitude information.
However, the correction of background white noise also needs to be done on an ongoing basis, as they may change over time, temperature, and the environment.
Inertial sensors often generate errors such as offset error, proportional error, and background white noise during the measurement process. In order to correct these errors, we need to perform calibration. Calibration includes static calibration and dynamic calibration, which can help us reduce errors and improve the accuracy of measurements. However, because errors can vary with time, temperature, and environment, calibration cannot fully address the accumulation of displacement errors.
Therefore, for the positioning problem of unmanned vehicles, the use of inertial sensors alone is often not enough. We need to comprehensively consider other sensors, such as GPS, lidar, etc., and fuse positioning with inertial sensors to improve the accuracy and stability of unmanned vehicle positioning. At the same time, for long-term positioning tasks, continuous calibration and compensation are also necessary to ensure that the positioning error is controlled within a reasonable range.
Therefore, for application scenarios such as unmanned vehicles, we need to comprehensively consider the advantages and disadvantages of various sensors, and adopt appropriate data fusion and calibration methods to achieve accurate and stable positioning and navigation capabilities. #HeadlineCreationChallenge##我要上条#