An Object-Oriented Asynchronous Kalman Filter with Outlier Rejection for Autonomous Tractor Navigation
In the near future, precision farming operations will increasingly rely on the automatic steering and navigation capabilities of agricultural vehicles. Auto-steering reliability and precision depend on the continuous availability of valid and accurate state data, provided at a frequency at least equal to the sampling rate of the tracking controller. The data provided by any single sensor (e.g., GPS) is inadequate for long term autonomous steering. The reasons are occasional signal outage (availability) and exceedingly large noise content (validity); either may last for time periods which are very long in comparison to the controller’s sampling rate. The Kalman filter is a well-established technique to combine data from different sensors, in order to improve the availability and precision of the overall localization system. Typical filter implementations are platform specific, and do not address in a systematic way the issues of different sensor sampling rates and data validity. In this paper the implementation of an object-oriented, asynchronous, outlier-rejecting, extended Kalman filter is presented, along with simulation and experimental results, which test its correctness and evaluate its performance.
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