2134/25981
Harshana Dantanarayana
Harshana
Dantanarayana
Jonathan Huntley
Jonathan
Huntley
Improved maximum likelihood estimation of object pose from 3D point clouds using curves as features
Loughborough University
2017
Pose estimation
Maximum likelihood
Curve features
Edge features
Surface features
Point clouds
Mechanical Engineering not elsewhere classified
2017-08-08 10:39:04
Conference contribution
https://repository.lboro.ac.uk/articles/conference_contribution/Improved_maximum_likelihood_estimation_of_object_pose_from_3D_point_clouds_using_curves_as_features/9559169
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.