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Key-layered normal distributions transform for point cloud registration

1 1 1 1 1 Rating 4.90 (70 Votes)

PROJECT TITLE :

Key-layered normal distributions transform for point cloud registration

ABSTRACT:

A replacement scan matching algorithm is proposed using the concept of key layers. In the traditional multi-layered traditional distributions rework (MLNDT), the amount of layers and iterations per layer are fixed and mismatches in point clouds occur due to the restricted variety of optimising iterations per layer. Moreover, the accuracy of registration is low and the number of layers is heuristically determined in MLNDT. The proposed key-layered traditional distributions remodel (KLNDT) works well with each enhanced success rate and accuracy. It is additionally doable for KLNDT to register in higher layers than the traditional MLNDT.


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Key-layered normal distributions transform for point cloud registration - 4.9 out of 5 based on 70 votes

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