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Presently, core 3D registration technologies for augmented reality have problems like low accuracy and poor tracking stability in natural environments pertaining to mass customization and intelligent manufacturing, resulting in error display or poor visual performance. (1) A non-linear scale space was used to alleviate the problem associated with scale invariance, the relevant calculations and construction methods were studied. The adaptive non-maximal suppression method was examined, which reduced redundancy of ORB (Oriented FAST and Rotated BRIEF) feature points. (2) The method of combining nonlinear local descriptors with LK method is studied to improve the low stability problem against Light change by LK method only, and a forward-backward error detection method was studied to evaluate feature point tracking results. (3) The improved ORB and LK methods are used to track the target and achieve data fusion. Then, the fused data is voted and clustered by means of consistent voting, only the maximum number of clusters within the threshold is left as the final tracking result to realize 3D registration. Finally, the paper validates the proposed method through the natural environment dataset of open source. The dimensions of verification include challenging scales, rotation changes, perspective changes, motion blur, occlusion, and out-of-view object.

A Hybrid 3D Registration Method of Augmented Reality for Intelligent Manufacturing