Click on “Download PDF” for the PDF version or on the title for the HTML version.
If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.
Sensor Fusion for Roll and Pitch Estimation Improvement of an Agricultural Sprayer Vehicle
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Paper number 061159, 2006 ASAE Annual Meeting . (doi: 10.13031/2013.20644) @2006
Authors: Lav Ramchandra Khot, Lie Tang, Brain L Steward, Shufeng Han
Keywords: DEM, Roll, Pitch, Auto-regressive model, EKF
Sensor fusion technique has been commonly used for improving the navigation of
autonomous agricultural vehicles by means of combining complimentary sensors mounted on such
vehicles for the position and attitude angle measurements. In this research, sensor fusion via an
Extended Kalman Filter (EKF) was used to integrate the attitude angle estimates from the Digital
Elevation Models (DEMs) and Terrain Compensation Module (TCM) sensor to improve the roll and
pitch angle measurements of a self propelled sprayer. The fusion algorithm was also developed to
improve the three-dimensional positioning of the sprayer, in particular the elevation measurements of
a GPS receiver mounted on the sprayer. Vehicle attitude and field elevation were measured at two
speeds, 5.6 km/h and 9.6 km/h, using a set of onboard sensors including a real-time kinematic-differential GPS receiver (RTK-DGPS), a TCM sensor and an Inertial Measurement Unit (IMU). A
second order auto-regressive (AR) model was developed to model the TCM roll and GPS-based
pitch errors. The derived error states were incorporated into the EKF algorithm and the measurement
noise covariance was estimated from the AR model, which limited the fine tuning of noise covariance
to the process noise covariance only.