A tightly integrated image-aided Inertial Navigation System (INS), which copes with GNSS failure, is tested with a realistic data set from an Octocopter. The system integrates the inertial sensor data with position tracks of image feature points over an image sequence in an error-state extended Kalman filter (EKF). The Octocopter is equipped with a rig of three cameras in the horizontal direction with overlapping fields of view. Our main aim is to utilize the data from the three cameras as a single-sensor data. However, as an intermediate experiment, the cameras are considered as three individual sensors and the performance of the image-aided INS with the different combinations of data integration is analyzed in this study. The image-aided INS reduced the drift drastically compared to the drift in free-inertial when integrating the image data sets separately or in combinations. However, the combination of all three data sets together performed poorer than the other combinations, probably due to correlated errors that are not adequately modeled by the current EKF.
A typical unmanned aerial system combines an Inertial Navigation System (INS) and a Global Navigation Satellite System (GNSS) for navigation. When the GNSS signal is unavailable, the INS errors grow over time and eventually become unacceptable as a navigation solution. Here we investigate an image-aided inertial navigation system to cope with GNSS failure. The system is based on tightly integrating inertial sensor data with position data of image-featurepoints that corresponds to landmarks over an image sequence. The aim of this experiment is to study the challenges and the performance of the image-aided inertial navigation system in realistic flight with an Octocopter. The system demonstrated the ability to cope with the GNSS failure by reducing the position drift drastically compared to the position drift of free-inertial.
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