We address the problem of low amplitude oscillatory motion detection through different low-cost sensors: a LIS3LV02DQ MEMS accelerometer, aMicrosoft Kinect v2 range camera, and a uBlox 6 GPS receiver. Several tests were performed using a one-direction vibrating table with different oscillation frequencies (in the range 1.5–3Hz) and small challenging amplitudes (0.02m and 0.03m). A Mikrotron EoSens high-resolution camera was used to give reference data. A dedicated software tool was developed to retrieve Kinect v2 results. The capabilities of the VADASE algorithm were employed to process uBlox 6 GPS receiver observations. In the investigated time interval (in the order of tens of seconds) the results obtained indicate that displacements were detected with the resolution of fractions of millimeters with MEMS accelerometer and Kinect v2 and few millimeters with uBlox 6. MEMS accelerometer displays the lowest noise but a significant bias, whereas Kinect v2 and uBlox 6 appear more stable. The results suggest the possibility of sensor integration both for indoor (MEMS accelerometer + Kinect v2) and for outdoor (MEMS accelerometer + uBlox 6) applications and seem promising for structural monitoring applications.

Exploiting performance of different low-cost sensors for small amplitude oscillatory motion monitoring: preliminary comparisons in view of possible integration

RAVANELLI, ROBERTA;
2016

Abstract

We address the problem of low amplitude oscillatory motion detection through different low-cost sensors: a LIS3LV02DQ MEMS accelerometer, aMicrosoft Kinect v2 range camera, and a uBlox 6 GPS receiver. Several tests were performed using a one-direction vibrating table with different oscillation frequencies (in the range 1.5–3Hz) and small challenging amplitudes (0.02m and 0.03m). A Mikrotron EoSens high-resolution camera was used to give reference data. A dedicated software tool was developed to retrieve Kinect v2 results. The capabilities of the VADASE algorithm were employed to process uBlox 6 GPS receiver observations. In the investigated time interval (in the order of tens of seconds) the results obtained indicate that displacements were detected with the resolution of fractions of millimeters with MEMS accelerometer and Kinect v2 and few millimeters with uBlox 6. MEMS accelerometer displays the lowest noise but a significant bias, whereas Kinect v2 and uBlox 6 appear more stable. The results suggest the possibility of sensor integration both for indoor (MEMS accelerometer + Kinect v2) and for outdoor (MEMS accelerometer + uBlox 6) applications and seem promising for structural monitoring applications.
BENEDETTI, ELISA; RAVANELLI, ROBERTA; MORONI, Monica; NASCETTI, ANDREA; CRESPI, Mattia Giovanni
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/744862
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