GNSS( global navigation satellite systems) are unavailable in challenging environments such as urban canyon and indoor locations due to signal blocking and jamming. Camera / IMU( inertial measurement units) integrated...GNSS( global navigation satellite systems) are unavailable in challenging environments such as urban canyon and indoor locations due to signal blocking and jamming. Camera / IMU( inertial measurement units) integrated navigation systems can be alternatives to GNSS. In this paper,a tightly coupled Camera / IMU algorithm modeled by IEKF( iterated extended kalman filter) is presented. This tight integration approach uses image generated pixel coordinates to update the Kalman Filter directly. The developed algorithm is verified by a hybrid simulation,i.e. using inertial data from field test to fuse with simulated image feature measurements. The results show that the tight approach is superior to the loose integration when the image measurements are insufficient( i.e. less than three ground control points).展开更多
基金Sponsored by the National High Technology Research and Development Program(Grant No.2012AA12A209)the National Natural Science Foundation of China(Grant No.41174028,41374033)+2 种基金the Key Laboratory Development Fund from the Ministry of Education of China(Grant No.618-277176)the LIESMARS Special Research Fund,the Research Start-up Fund from Wuhan Univesity(Grant No.618-273438)the Fundamental Research Funds for the Central Universities(Grant No.201161802020002)
文摘GNSS( global navigation satellite systems) are unavailable in challenging environments such as urban canyon and indoor locations due to signal blocking and jamming. Camera / IMU( inertial measurement units) integrated navigation systems can be alternatives to GNSS. In this paper,a tightly coupled Camera / IMU algorithm modeled by IEKF( iterated extended kalman filter) is presented. This tight integration approach uses image generated pixel coordinates to update the Kalman Filter directly. The developed algorithm is verified by a hybrid simulation,i.e. using inertial data from field test to fuse with simulated image feature measurements. The results show that the tight approach is superior to the loose integration when the image measurements are insufficient( i.e. less than three ground control points).
