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We present a 3D reconstruction technique designed to support an autonomously navigated unmanned aerial system (UAS). The algorithm presented focuses on the 3D reconstruction of a scene using images from a single moving camera and can be used to construct a point cloud model of unknown areas. The reconstruction process, resulting in a point cloud model is computed using a feature point matching process and depth triangulation analysis, is a six step process. The first step is feature extraction from each frame of video; a neighborhood-magnitude-direction dependent matching procedure is applied to track feature points through subsequent frames. The distance a feature point travels, in pixels, becomes the feature disparity which can be translated into depth. The Cartesian depth coordinate, in the z direction, is determined using the disparity values, while the x and y coordinates are determined using the focal length information of the camera. The process consists of determining the size of the image at a particular depth and computing the width and height, x and y directions, for each feature point. The final output is a point cloud, a collection of points accurately positioned within a model. With enough points, surfaces and textures can be added to create a realistic model. An autonomous navigation control system utilizes the resulting visually reconstructed scene, centered at the current camera location, to either register its position within a known 3D model, or for obstacle avoidance and area exploration while mapping an unknown environment. The presented reconstruction algorithm forms a foundation for computer vision self-positioning techniques within a known environment without the use of GPS or any other sensor. The suitability of the reconstruction for mapping tasks is to be evaluated using ground-truth measurements of actual objects.
Vijayan K. Asari
Primary Advisor's Department
Electrical and Computer Engineering
Stander Symposium poster
Diskin, Yakov, "Automatic Scene Rendering for Unmanned Aerial Systems" (2013). Stander Symposium Posters. 248.