In this article, an integrated global positioning system (GPS), inertial navigation system (INS), and visual navigation system (VNS) navigationmethod based on an adaptive federal Kalman filter (KF) is presented to improve positioning accuracy for a rice transplanter operating in a paddy field. Theproposed method used GPS/VNS to aid the INS and reduce the influence of the accumulated error of the INS on navigation accuracy. An adaptive federal KFalgorithm was designed to fuse navigation information from different sensors. The information distribution factor of each local filter was obtained adaptivelyon the basis of its own error covariance matrix. Computer simulation and transplanter tests were conducted to verify the proposed method. Results showedthat the proposed method provided accurate and reliable navigation information outputs and achieved better navigation performance compared with singleGPS navigation and an integrated method based on a conventional federal KF.
Bibliographical noteFunding Information:
This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFD0700304) and the Key Research andDevelopment Program of Anhui Province (Grant Nos. 202004a06020016 and 202004a06020061).
This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFD0700304) and the Key Research and Development Program of Anhui Province (Grant Nos. 202004a06020016 and 202004a06020061).
© 2021 American Society of Agricultural and Biological Engineers. All rights reserved.
Copyright 2021 Elsevier B.V., All rights reserved.
- Federal Kalman filter
- Information distribution factor
- Information fusion
- Integrated navigation
ASJC Scopus subject areas
- Food Science
- Biomedical Engineering
- Agronomy and Crop Science
- Soil Science