Abstract. To estimate the position and heading angle of mobile robot precisely, an measurement variable estimation model was proposed to adapt any angle. Fusing the predictive value of odometry and measurement data of multiple sensors by the Extended Kalman Filtering (EKF) for reducing the accumulative error by using only traditional odometry. The proposed models is verified by Matlab simulation and experimental results.
The localization and navigation technology are the key factors in the research of mobile robots. With the demand
of smart manufacturing industry and the development of robotics technology, the importance of mobile robot has become increasingly
prominent. Mobile robot positioning research is mostly based on odometry, however, it has cumulative errors that would affect the
accuracy of positioning results.
This paper describes an improved measurement model that suitable from 0° to 180° and used this model in the
Extended Kalman Filter (EKF) and Unscented Kalman Filter(UKF) time update step respectively, the method can address the
interference of kinematics model predicted position and heading angle, both of them are easily disturbed by noises and other
factors. Designing a tracked mobile robot as experimental platform to collect the raw data, conducting experimental research
including the performance of hardware platform and autonomous obstacle avoidance, the real-time and stability of remote data
interaction, and the accuracy of optimal pose estimation. The simulation results have been verified the accuracy of the improved
measurement model applied to UKF.
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