An Integrated Navigation Method Based on an Adaptive Federal Kalman Filter for a Rice Transplanter

Abstract

Highlights A GPS/INS/VNS integrated navigation system to improve navigation accuracy. An adaptive federal Kalman filter with the adaptive information distribution factor to fuse navigation information.  Detection of seedling row lines based on sub-regional feature points clustering. A modified rice transplanter as an automatic navigation experimental platform. In this study, a global positioning system (GPS)/inertial navigation system (INS)/visual navigation system (VNS)-integrated navigation method based on an adaptive federal Kalman filter (KF) was presented to improve positioning accuracy for rice transplanter operating in paddy field. The proposed method used GPS/VNS to aid INS and reduce the influence of the accumulated error of the INS on navigation accuracy. An adaptive federal KF algorithm was designed to fuse navigation information from different sensors. The information distribution factor of each local filter was obtained adaptively on the basis of its own error covariance matrix. Computer simulation and the transplanter test were conducted to verify the proposed method. Results showed that the proposed method could provide accurate and reliable navigation information outputs, and achieve better navigation performance compared with that of single GPS navigation and integrated method based traditional federal KF.