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

TLDR

This paper presents a new method for improving the accuracy of inertial measurement units (IMUs) mounted on land vehicles. The method exploits vehicle nonholonomic constraints to generate velocity observations that aid IMU alignment and forward velocity estimation, and is implemented in real time using a multiple‑observation information‑filter algorithm, with theoretical analysis and experimental comparison against a nonlinear implementation and an IMU/GPS navigation system. The algorithm achieves accurate IMU alignment and velocity estimation without external sensors when observability conditions are met, as demonstrated by theoretical analysis and experiments that show improved performance over standard integration, tighter navigation loops, and extended GPS‑outage endurance.

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.