This paper presents a realtime recursive algorithm that can estimate, starting from an unknown pose, the mounting angles (roll, pitch and yaw) of an inertial sensor unit using accelerations and angular velocities. We analyze the use case of telematic boxes (E-Box) that are mounted on ground vehicles for safety reason (like E-call or automatic crash detection) or driving style monitoring. In order to work properly and record meaningful data, the box reference frame needs to be correctly aligned with the vehicle one. The proposed algorithm aligns the two reference frame online while the car is running throughout a series of filters and data point selection logics. Results show that the algorithm is robust with respect to any box mounting position or vehicle, with, on average, a convergence time of less than 20 minutes to the correct angles.

GNSS-free Online Calibration of Inertial Measurement Units in Road Vehicles

Rodrigo Senofieni;Matteo Corno;Silvia Carla Strada;Sergio Matteo Savaresi;
2023-01-01

Abstract

This paper presents a realtime recursive algorithm that can estimate, starting from an unknown pose, the mounting angles (roll, pitch and yaw) of an inertial sensor unit using accelerations and angular velocities. We analyze the use case of telematic boxes (E-Box) that are mounted on ground vehicles for safety reason (like E-call or automatic crash detection) or driving style monitoring. In order to work properly and record meaningful data, the box reference frame needs to be correctly aligned with the vehicle one. The proposed algorithm aligns the two reference frame online while the car is running throughout a series of filters and data point selection logics. Results show that the algorithm is robust with respect to any box mounting position or vehicle, with, on average, a convergence time of less than 20 minutes to the correct angles.
2023
3rd Modeling, Estimation and Control Conference MECC 2023
Telematic box, IMUattitude estimation,mounting angles,Calibration,Recursive Least Squares,experimental validation
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S240589632302342X-main.pdf

Accesso riservato

Descrizione: Paper
: Publisher’s version
Dimensione 1.31 MB
Formato Adobe PDF
1.31 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1259776
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact