Object recognition in 3D point clouds with maximum likelihood estimation

Harshana G. Dantanarayana; Jonathan M. Huntley

doi:10.1117/12.2185227

22 June 2015 Object recognition in 3D point clouds with maximum likelihood estimation

Harshana G. Dantanarayana, Jonathan M. Huntley

Proceedings Volume 9530, Automated Visual Inspection and Machine Vision; 95300F (2015) https://doi.org/10.1117/12.2185227
Event: SPIE Optical Metrology, 2015, Munich, Germany

Abstract

A novel technique for object recognition and localization within a 3D point cloud has been developed, by constructing a likelihood function for the pose vector of a known model in a measured scene. The function is based on surface features in the model and corresponding surface features detected in the scene. Using an optimization algorithm, the maximum of the function was found, corresponding to the 6 degree of freedom (DOF) pose of the model within the scene even in the presence of significant clutter.

Citation Download Citation

Harshana G. Dantanarayana and Jonathan M. Huntley "Object recognition in 3D point clouds with maximum likelihood estimation", Proc. SPIE 9530, Automated Visual Inspection and Machine Vision, 95300F (22 June 2015); https://doi.org/10.1117/12.2185227

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