Point set registration for assembly feature pose estimation using simulated annealing nested Gauss-Newton optimization

Abstract

Purpose This paper aims to propose a fast and robust 3D point set registration method for pose estimation of assembly features with few distinctive local features in the manufacturing process. Design/methodology/approach The distance between the two 3D objects is analytically approximated by the implicit representation of the target model. Specifically, the implicit B-spline surface is adopted as an interface to derive the distance metric. With the distance metric, the point set registration problem is formulated into an unconstrained nonlinear least-squares optimization problem. Simulated annealing nested Gauss-Newton method is designed to solve the non-convex problem. This integration of gradient-based optimization and heuristic searching strategy guarantees both global robustness and sufficient efficiency. Findings The proposed method improves the registration efficiency while maintaining high accuracy compared with several commonly used approaches. Convergence can be guaranteed even with critical initial poses or in partial overlapping conditions. The multiple flanges pose estimation experiment validates the effectiveness of the proposed method in real-world applications. Originality/value The proposed registration method is much more efficient because no feature estimation or point-wise correspondences update are performed. At each iteration of the Gauss–Newton optimization, the poses are updated in a singularity-free format without taking the derivatives of a bunch of scalar trigonometric functions. The advantage of the simulated annealing searching strategy is combined to improve global robustness. The implementation is relatively straightforward, which can be easily integrated to realize automatic pose estimation to guide the assembly process.

Optimization method for the assembly pose of parts considering manufacturing deviations and contact deformations

“…Skin model shapes can reflect the actual manufacturing situation of components and can be used to build the digital twin model in the product assembly process. For the parts with complex structures, the point cloud model considering the surface morphology is usually used to establish the optimization model for achieving the prediction of the assembly accuracy and guiding the following decision (Chen et al , 2021). In addition, the situation of parts interference should be taken into account during the assembly process as the constraint condition for the optimization.…”