Reconstructing Normal Section Profiles of 3-D Revolving Structures via Pose-Unconstrained Multiline Structured-Light Vision

Reconstructing normal section profiles is an effective approach at monitoring the shape of a 3-D revolving-symmetry structure. The existing reconstruction methods typically require sensor to work in constrained pose and thus suffer from poor flexibility limited view-angle. This article proposes pose-unconstrained profile framework for structures, which estimates using multiple general acquired by multiline structured light vision sensor. First, we establish model estimate revolving axis calculate corresponding points. Then, embedded into iterative algorithm optimize points accurate profile. Simulations show that our applicable structures various shapes robust against noise local surface defects. Real experiments were conducted on reconstructing wheel. results demonstrate reaches mean precision 0.065 mm repeatability with STD 0.007 mm. It also position