Improved maximum likelihood estimation of object pose from 3D point clouds using curves as features

Harshana G. Dantanarayana; Jonathan M. Huntley

doi:10.1117/12.2270197

26 June 2017 Improved maximum likelihood estimation of object pose from 3D point clouds using curves as features

Harshana G. Dantanarayana, Jonathan M. Huntley

Proceedings Volume 10334, Automated Visual Inspection and Machine Vision II; 103340D (2017) https://doi.org/10.1117/12.2270197
Event: SPIE Optical Metrology, 2017, Munich, Germany

Abstract

Object recognition and pose estimation is a fundamental problem in automated quality control and assembly in the manufacturing industry. Real world objects present in a manufacturing engineering setting tend to contain more smooth surfaces and edges than unique key points, making state-of-the-art algorithms that are mainly based on key-point detection, and key-point description with RANSAC and Hough based correspondence aggregators, unsuitable. An alternative approach using maximum likelihood has recently been proposed in which surface patches are regarded as the features of interest1 . In the current study, the results of extending this algorithm to include curved features are presented. The proposed algorithm that combines both surfaces and curves improved the pose estimation by a factor up to 3×, compared to surfaces alone, and reduced the overall misalignment error down to 0.61 mm.

Citation Download Citation

Harshana G. Dantanarayana and Jonathan M. Huntley "Improved maximum likelihood estimation of object pose from 3D point clouds using curves as features", Proc. SPIE 10334, Automated Visual Inspection and Machine Vision II, 103340D (26 June 2017); https://doi.org/10.1117/12.2270197

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