Vision-based positioning and terrain mapping by global alignment for UAVs

Construction of 3D topographic maps from stereo or monocular video, over coverage areas of kilometer scale, taken by low-altitude airborne platforms is addressed. Two computational frameworks for these two cases are considered, accommodating the online processing of video along the path. In these formulations, stereo disparity information enables the computation of 3D motions and depth maps to be done more readily, however, monocular motion cues provide similar accuracy with more computational steps. The critical issue is to overcome the drift error, which is inherent of the causal frame-to frame motion estimation, as the video frames are acquired. A novel global alignment scheme is proposed, aimed at determining the 3D trajectory most consistent with the estimated 3D motions between pairs of nearby positions. Performance is demonstrated based on experiment with a sequence of 5000 stereo pairs, simulating aerial photographic data from an airborne platform flying at 110 m above (the reference plane of) a 1 km /spl times/ 1 km terrain with 5-65m elevation. Maximum geo-referenced positioning accuracy is roughly 2 m, with elevation error of 1 m or less over 95% of the terrain.