VISION-AIDED AERIAL NAVIGATION

An aerial vehicle is navigated using vision-aided navigation that classifies regions of acquired still image frames as featureless or feature-rich, and thereby avoids expending time and computational resources attempting to extract and match false features from the featureless regions. The classification may be performed by computing a texture metric as by testing widths of peaks of the autocorrelation function of a region against a threshold, which may be an adaptive threshold, or by using a model that has been trained using a machine learning method applied to a training dataset comprising training images of featureless regions and feature-rich regions. Such machine learning method can use a support vector machine. The resultant matched feature observations can be data-fused with other sensor data to correct a navigation solution based on GPS and/or IMU data.