2134/26062
Harshana Dantanarayana
Harshana
Dantanarayana
Jonathan Huntley
Jonathan
Huntley
Object recognition and localisation from 3D point clouds by maximum likelihood estimation
Loughborough University
2017
Fringe projection 3D scanning
Pose estimation
Object recognition
Industrial inspection
Mechanical Engineering not elsewhere classified
2017-08-15 13:58:06
Journal contribution
https://repository.lboro.ac.uk/articles/journal_contribution/Object_recognition_and_localisation_from_3D_point_clouds_by_maximum_likelihood_estimation/9564152
We present an algorithm based on maximum
likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike ‘interest point’ based algorithms which normally discard such data. Compared to the 6D Hough transform it has negligible memory requirements, and is
computationally efficient compared to iterative closest point (ICP) algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through
appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degree of freedom
(DOF) example is given, followed by a full 6 DOF analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an rms alignment error as low as 0:3 mm.