Vision-aided terrain referenced navigation for unmanned aerial vehicles using ground features

Abstract

A vision-aided terrain referenced navigation (VATRN) approach is addressed for autonomous navigation of unmanned aerial vehicles (UAVs) under GPS-denied conditions. A typical terrain referenced navigation (TRN) algorithm blends inertial navigation data with measured terrain information to estimate vehicle’s position. In this paper, a low-cost inertial navigation system (INS) for UAVs is supplemented with a monocular vision-aided navigation system and terrain height measurements. A point mass filter based on Bayesian estimation is employed as a TRN algorithm. Homograpies are established to estimate the vehicle’s relative translational motion using ground features with simple assumptions. And the error analysis in homography estimation is explored to estimate the error covariance matrix associated with the visual odometry data. The estimated error covariance is delivered to the TRN algorithm for robust estimation. Furthermore, multiple ground features tracked by image observations are utilized as multiple height measurements to improve the performance of the VATRN algorithm.

Terrain-Referenced Navigation using an Interferometric Radar Altimeter

“…To verify the performance of the measurement models (11) and (13), the time history of the RMS position errors of the proposed method and the existing method on the rough terrain and the smooth terrain will be presented. As mentioned earlier, the existing method is denoted as 'TRN-IRA', and the proposed method is denoted as 'TRN-IRA-P.' Figure 6 depicts the time history of the RMS position errors of the navigation systems using DEM1.…”

“…In terms of estimators, various kinds of filters and methods have been applied to the TRN, e.g., Kalman filters [1][2][3] and Bayesian methods [4], and adaptations to specific problems have been incorporated [5][6][7][8] to improve flexibility and reliability of the navigation system. On the other hand, additional or alternative sensors such as airborne laser scanners [9,10] and cameras [11][12][13][14] have been used to measure terrain profile. These have addressed theoretical navigation capability in Global Navigation Satellite System (GNSS)-devoid scenarios.…”

Survey on Computer Vision for UAVs: Current Developments and Trends

“…It has also been developed a switching controller to stabilize the lateral position of the vehicle for both the detected and occluded road cases. In [142] a robust vision terrain referenced navigation method for UAV position estimation has been proposed, combining visual odometry by homography with point-mass filter based navigation algorithm. The data used in the process were obtained from a monocular camera, a radio altimeter and a terrain referenced elevation map.…”

Pigeon-inspired optimization and lateral inhibition for image matching of autonomous aerial refueling

“…Visual system can play an important part in the visual guidance, measurement, and detection. [4][5][6][7][8][9] In the process of flying boom AAR, the tanker aircraft has to recognize the boom and receptacle accurately. The requirements can be satisfied by the image matching (template matching) method, 10 which is a process of searching the right sub-image in the reference image according to the known template.…”