The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications

This paper presents a new method for improving the accuracy of inertial measurement units (IMUs) mounted on land vehicles. The algorithm exploits nonholonomic constraints that govern the motion of a vehicle on a surface to obtain velocity observation measurements which aid in the estimation of the a...

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Bibliographic Details
Published inIEEE transactions on robotics and automation Vol. 17; no. 5; pp. 731 - 747
Main Authors Dissanayake, G., Sukkarieh, S., Nebot, E., Durrant-Whyte, H.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.10.2001
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Online AccessGet full text
ISSN1042-296X
2374-958X
DOI10.1109/70.964672

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Abstract This paper presents a new method for improving the accuracy of inertial measurement units (IMUs) mounted on land vehicles. The algorithm exploits nonholonomic constraints that govern the motion of a vehicle on a surface to obtain velocity observation measurements which aid in the estimation of the alignment of the IMU as well as the forward velocity of the vehicle. It is shown that this can be achieved without any external sensing provided that certain observability conditions are met. A theoretical analysis is provided together with a comparison of experimental results between a nonlinear implementation of the algorithm and an IMU/GPS navigation system. This comparison demonstrates the effectiveness of the algorithm. The real time implementation is also addressed through a multiple observation inertial aiding algorithm based on the information filter. The results show that the use of these constraints and vehicle speed guarantees the observability of the velocity and the attitude of the inertial unit, and hence bounds the errors associated with these states. The strategies proposed provides a tighter navigation loop which can sustain outages of GPS for a greater amount of time as compared to when the inertial unit is used with standard integration algorithms.
AbstractList This paper presents a new method for improving the accuracy of inertial measurement units (IMUs) mounted on land vehicles. The algorithm exploits nonholonomic constraints that govern the motion of a vehicle on a surface to obtain velocity observation measurements which aid in the estimation of the alignment of the IMU as well as the forward velocity of the vehicle. It is shown that this can be achieved without any external sensing provided that certain observability conditions are met. A theoretical analysis is provided together with a comparison of experimental results between a nonlinear implementation of the algorithm and an IMU/GPS navigation system. This comparison demonstrates the effectiveness of the algorithm. The real time implementation is also addressed through a multiple observation inertial aiding algorithm based on the information filter. The results show that the use of these constraints and vehicle speed guarantees the observability of the velocity and the attitude of the inertial unit, and hence bounds the errors associated with these states. The strategies proposed provides a tighter navigation loop which can sustain outages of GPS for a greater amount of time as compared to when the inertial unit is used with standard integration algorithms
This paper presents a new method for improving the accuracy of inertial measurement units (IMUs) mounted on land vehicles. The algorithm exploits nonholonomic constraints that govern the motion of a vehicle on a surface to obtain velocity observation measurements which aid in the estimation of the alignment of the IMU as well as the forward velocity of the vehicle. It is shown that this can be achieved without any external sensing provided that certain observability conditions are met. A theoretical analysis is provided together with a comparison of experimental results between a nonlinear implementation of the algorithm and an IMU/GPS navigation system. This comparison demonstrates the effectiveness of the algorithm. The real time implementation is also addressed through a multiple observation inertial aiding algorithm based on the information filter. The results show that the use of these constraints and vehicle speed guarantees the observability of the velocity and the attitude of the inertial unit, and hence bounds the errors associated with these states. The strategies proposed provides a tighter navigation loop which can sustain outages of GPS for a greater amount of time as compared to when the inertial unit is used with standard integration algorithms.
This paper presents a new method for improving the accuracy of inertial measurement units (IMUs) mounted on land vehicles. In contrast to the typical techniques used for IMUs mounted on flight vehicles, the algorithm exploits nonholonomic constraints that govern the motion of a vehicle on a surface to obtain velocity observation measurements which aid in the estimation of the alignment of the IMU as well as the forward velocity of the vehicle.
Author Nebot, E.
Sukkarieh, S.
Durrant-Whyte, H.
Dissanayake, G.
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  surname: Durrant-Whyte
  fullname: Durrant-Whyte, H.
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Snippet This paper presents a new method for improving the accuracy of inertial measurement units (IMUs) mounted on land vehicles. The algorithm exploits nonholonomic...
This paper presents a new method for improving the accuracy of inertial measurement units (IMUs) mounted on land vehicles. In contrast to the typical...
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SubjectTerms Algorithm design and analysis
Algorithms
Automation
Engineering
Geographic information systems
Global Positioning System
Inertial
Information filters
Land
Land vehicles
Measurement
Measurement units
Motion estimation
Motion measurement
Navigation
Observability
Satellite navigation systems
Vehicles
Velocity measurement
Title The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications
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https://www.proquest.com/docview/234909972
https://www.proquest.com/docview/914646403
Volume 17
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