Accurate and rapid alignment of laser scanned 3D surface using TSK-type neural-fuzzy network-based coarse-to-fine strategy

Aligning a laser scanned three-dimensional (3D) surface is considered a critical step in object recognition, shape analysis, and automatic visual inspection. Two major concerns for the alignment task are execution time and alignment accuracy. Recently, neural network-based methods have become very popular due to their high efficiency. However, such methods experience difficulty in reaching high accuracy because the use of principal component analysis (PCA) to perform coarse alignment causes a large alignment error. Thus, a TSK-type neural-fuzzy network (TNFN)-based coarse-to-fine 3D surface alignment scheme is proposed in the current paper. Compared with traditional neural network-based approaches, the proposed method provides a coarse-to-fine alignment approach to ensure the accurate pose estimated by TNFN in the coarse phase, as well the high alignment speed provided by TNFN-based surface modeling in the fine phase. Experimental results demonstrate the superior performance of the proposed 3D surface alignment system over existing systems.

► NN-based coarse-to-fine strategy is proposed for 3D surface alignment problem.
► In coarse phase, the pose estimation method will increase the alignment accuracy.
► In fine phase, surface modeling and downhill simplex method ensure the efficiency.
► The coarse-to-fine strategy exhibits better performance than others.