Object Displacement Estimation With the Use of Microelectromechanical Accelerometer

Ursel, Tomasz

doi:10.1109/msm49833.2020.9202145

Cited by 1 publication

(2 citation statements)

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“…For such situations, the zero velocity (ZV) state detection algorithm may be also determined on the basis of, for example, acoustic noise accompanying the motion. It is an essential factor providing decent information about an object’s movement [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…Improving the accelerometer’s existing algorithm for zero-velocity states identification and velocity correction is another new important aspect researched in this article. The basic preliminary version of the accelerometers data processing algorithm was described in our previous work [ 24 , 30 ]. Specifically, the accelerometer’s measured signals are processed according to an algorithm, where ZV detection is achieved through acceleration and velocity data observations and, in addition, the velocity correction is further supplemented by applying acceleration symmetrization (linearization), improving the obtained results.…”

Section: Introductionmentioning

confidence: 99%

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Displacement Estimation Based on Optical and Inertial Sensor Fusion

Ursel

Olinski

2021

Sensors

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This article aims to develop a system capable of estimating the displacement of a moving object with the usage of a relatively cheap and easy to apply sensors. There is a growing need for such systems, not only for robots, but also, for instance, pedestrian navigation. In this paper, the theory for this idea, including data postprocessing algorithms for a MEMS accelerometer and an optical flow sensor (OFS), as well as the developed complementary filter applied for sensor fusion, are presented. In addition, a vital part of the accelerometer’s algorithm, the zero velocity states detection, is implemented. It is based on analysis of the acceleration’s signal and further application of acceleration symmetrization, greatly improving the obtained displacement. A test stand with a linear guide and motor enabling imposing a specified linear motion is built. The results of both sensors’ testing suggest that the displacement estimated by each of them is highly correct. Fusion of the sensors’ data gives even better outcomes, especially in cases with external disturbance of OFS. The comparative evaluation of estimated linear displacements, in each case related to encoder data, confirms the algorithms’ operation correctness and proves the chosen sensors’ usefulness in the development of a linear displacement measuring system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%