This paper presents a novel algorithm that uses scanning LiDAR range data, computer vision features, and a reference database to provide aircraft position estimations to update drifting INS estimates. The algorithm uses a single calibrated scanning LiDAR to sample the range and angle to the ground as an aircraft flies, forming a point cloud of coordinates with a presumed position error due to INS drift. The point cloud is orthorectifed into a coordinate system common to a previously recorded reference of the flyover region. The point cloud is then interpolated into a digital elevation model of the ground. Range-based SIFT features are then extracted. Features common to both the collected and reference range images are selected using SIFT descriptor matching. Poorly matched features are filtered out using RANSAC outlier removal, and surviving features are projected back to their source coordinates in the original point cloud. The point cloud features are used to calculate a least squares transform that aligns the collected features to the reference features. The translation that shifts the collected features to best match the reference database then represents an estimate of INS position error. Applying the correspondence that best aligns the ground features is then applied to the nominal aircraft position, creating a new position estimate. The algorithm was tested on legacy flight data and produces position estimates within 10 meters of truth using threshold conditions.
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