INS/Odometer Land Navigation by Accurate Measurement Modeling and Multiple-Model Adaptive Estimation
This work addresses incremental improvements in autonomous navigation for land vehicles by refining existing INS/odometer integration methods.
The paper tackled the problem of improving land vehicle navigation accuracy by analyzing odometer error characteristics and proposing three measurement models integrated with a Kalman filter and multiple-model adaptive estimation. The results showed that the standard pulse velocity model performed best, while the accumulated pulse model was most effective with enhancement, as verified through simulations and long-distance experiments.
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.