A new approach to geometrical feature assessment for ICP-based pose measurement: Continuum shape constraint analysis

This paper presents a generalization of closest-point constraint analysis called Continuum Shape Constraint Analysis (CSCA) that can be used to asses the suitability of whole objects or object features for range data scanning and subsequent pose estimation. "Directional CSCA" (D-CSCA) is proposed to specifically address pose estimation accuracy via the ICP (Iterated Closest-Point) family of algorithms. Constraint analysis based on Noise Amplification Index (NAI) is used. In the D-CSCA formulation, the continuum nature of the underlying shape registration renders the resulting gradient matrix and NAI thereof as pure properties of the feature, dependent on viewpoint but independent of the viewing instrument.