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. 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. 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 observations used in the information filter include position and velocity of the vehicle from a GPS unit, speed from a wheel encoder, and virtual observations due to the constraints on the motion of the vehicle. 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 observations from the GPS unit adds extra information to the estimate of these states as well as providing observability of position. The strategies proposed in this paper provides for 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.