6D Pose Estimation for Flexible Production with Small Lot Sizes based on CAD Models using Gaussian Process Implicit Surfaces

abstract

We propose a surface-to-surface (S2S) point registration algorithm by exploiting the Gaussian Process Implicit Surfaces for partially overlapping 3D surfaces to estimate the 6D pose transformation. Unlike traditional approaches, that separate the corresponding search and update steps in the inner loop, we formulate the point registration as a nonlinear non-constraints optimization problem which does not explicitly use any corresponding points between two point sets. According to the implicit function theorem, we form one point set as a Gaussian Process Implicit Surfaces utilizing the signed distance function, which implicitly creates three manifolds. Points on the same manifold share the same function value, indicated as 1, 0, -1. The problem is thus converted into finding a rigid transformation that minimizes the inherent function value. This can be solved by using a Gauss-Newton (GN) or Levenberg-Marquardt (LM) solver. In the case of a partially overlapping 3D surface, the Fast Point Feature Histogram (FPFH) algorithm is applied to both point sets and a Principal Component Analysis (PCA) is performed on the result. Based on this, the initial transformation can then be computed. We conduct experiments on multiple point sets to evaluate the effectiveness of our proposed approach against existing state-of-the-art methods.