Implementation and Comparison of Attitude Estimation Methods for Agricultural Robotics

Abstract

The field of precision agriculture increasingly utilize and develop robotics for various applications, many of which are dependent on high accuracy localization and attitude estimation. Special attention has been put towards full attitude estimation by low-cost sensors, in relation to the development of an autonomous field robot. Quaternions have been chosen due to its continuous nature, and with respect to applications in the pipeline with on other platforms. The performance and complexity of two approaches to attitude estimation has been investigated: One Multiplicative Extended Kalman Filter (MEKF) and one non-linear observer. Both were implemented on an ARM Cortex M3 microcontroller with sensors for a Attitude Heading Reference System (AHRS), and benchmarked towards a relative high grade commercial AHRS device. The relative computational burden of the MEKF have been underlined, by execution times more than 10 times those of the non-linear estimator. The implementation complexity is also significantly lower for the non-linear observer, which facilitate test and verification through more transparent software.