A methodology for the performance evaluation of low-cost accelerometer and magnetometer sensors in geomatics applications

Abstract

This paper presents a methodology and its software implementation for the performance evaluation of low-cost accelerometer and magnetometer sensors for use in geomatics applications. A known mathematical calibration model has been adopted. The method was completed with statistical methodologies for adjusting observations and has been extended to calculate accuracies for the attitude, heading, and tilt angles estimation that are of interest to geomatics applications. The evaluation method consists of two stages. First, the evaluation method reviews the total magnitude of acceleration or the strength of the magnetic field. Second, the evaluation is more detailed and concerns the determination of mathematical parameters that describe both accelerometer and magnetometer working model. A software tool that implements the evaluation model has been developed and is applied both in accelerometer and magnetometer measurement data-sets acquired from a low-cost sensor system.

A Fusion Method for Combining Low-Cost IMU/Magnetometer Outputs for Use in Applications on Mobile Devices

“…A software utility called Inertial Measurement Unit Data Analyzer was developed in the Visual Basic programming language in order to debug the implementation and evaluate the fusion method. The utility captures the outputs acquired from the IMU and enhances them by applying corrections that emerge from a previous calibration procedure [ 14 ]. Next, the 3D orientation of the IMU body frame is estimated in real time and is expressed in the form of navigation angles, rotation matrix, and quaternions, while the navigation parameters are represented in numerical and visual form, as shown in Figure 6 .…”

“…Before using the raw sensor outputs to restore the DCM , corrections in the form of factors and additive constants that emerge by calibration procedures have to be applied to the raw data outputs [ 14 ].…”

“…In this research, the framework for designing the methodology for the synthesis of gyroscope, accelerometer, and magnetometer outputs using the Kalman filter was defined by the need to create an algorithm that gives precise results without high computational cost. For these reasons, a linear mathematical model was created and parameters, such as the gyroscope and accelerometer biases and scale factors, were treated as constants and were precalibrated in an external procedure [ 14 ]. In this way, it became possible to limit the unknown parameters (states) to 3.…”

“…In addition, there are possible errors due to the manufacture and assembly of the sensor system [ 9 ]. The quality of the sensors can be assured by analyzing large measurement samples [ 4 , 10 ], and their performance can be greatly improved by applying corrections that emerge from the calibration procedure [ 5 , 9 , 11 , 12 , 13 , 14 ]. In this way, the accuracy that can be achieved by these systems can be enhanced by one order of magnitude.…”

Laser Distance Sensors Evaluation for Geomatics Researches

“…An earlier work [8] showed how to build a 3D model for a heritage building using a photogrammetry combined with laser distances measurements by total station to make a ground referenced points. Another work [9] outlined a method with software to check the performance of low-cost sensors (accelerometer and magnetometer) for geomatics applications.…”