INS/Odometer Land Navigation by Accurate Measurement Modeling and Multiple-Model Adaptive Estimation

TL;DR

This paper improves land vehicle navigation by analyzing odometer error models and integrating multiple estimation techniques, demonstrating enhanced accuracy through simulations and experiments.

Contribution

It introduces three odometer measurement models and applies a multiple-model adaptive estimation approach to improve INS/odometer navigation accuracy.

Findings

Pulse velocity measurement yields the best performance.

Accumulated pulse measurement benefits most from MMAE.

Proposed methods are validated through simulations and experiments.

Abstract

Land vehicle navigation based on inertial navigation system (INS) and odometers is a classical autonomous navigation application and has been extensively studied over the past several decades. In this work, we seriously analyze the error characteristics of the odometer (OD) pulses and investigate three types of odometer measurement models in the INS/OD integrated system. Specifically, in the pulse velocity model, a preliminary Kalman filter is designed to obtain accurate vehicle velocity from the accumulated pulses; the pulse increment model is accordingly obtained by integrating the pulse velocity; a new pulse accumulation model is proposed by augmenting the travelled distance into the system state. The three types of measurements, along with the nonhonolomic constraint (NHC), are implemented in the standard extended Kalman filter. In view of the motion-related pulse error characteristics, the multiple model adaptive estimation (MMAE) approach is exploited to further enhance the performance. Simulations and long-distance experiments are conducted to verify the feasibility and effectiveness of the proposed methods. It is shown that the standard pulse velocity measurement achieves the superior performance, whereas the accumulated pulse measurement is most favorable with the MMAE enhancement.