Patent Document ID: 9858640
Application ID: 14800078
Patent Status: 1

Claim One:
1. A method for merging 3D point clouds from sparsely distributed viewpoints comprising: collecting a plurality of three dimensional (3D) point cloud data sets using a 3D sensor, each 3D point cloud data sets in a local reference frame of a viewpoint of the 3D sensor; downsampling the three dimensional (3D) point cloud data sets to form a plurality of downsampled three dimensional (3D) point cloud data sets; registering the downsampled 3D point cloud data sets to a global reference frame by computing an initial transform for rotating and translating each downsampled 3D point cloud data set from the local reference frame of the viewpoint of the downsampled 3D point cloud data set to the global reference frame; finding for each downsampled 3D point cloud data set corresponding points in other neighboring downsampled 3D point cloud data sets; mapping the corresponding points to the global reference frame using the initial transform; generating a list of each point in each downsampled 3D point cloud data set and the corresponding points; deriving an estimated transform that optimally maps each point in each downsampled 3D point cloud data set onto the corresponding points; and repeating the steps of generating and deriving an estimated transform for each downsampled 3D point cloud data set using a last derived estimated transform until the last derived estimated transform converges to a stable transform; and registering the plurality of three dimensional (3D) point cloud data sets to the global reference frame by: using the stable transform for rotating and translating each 3D point cloud data set from the local reference frame of the viewpoint of the 3D point cloud data set to the global reference frame; finding for each 3D point cloud data set corresponding points in other neighboring 3D point cloud data sets; mapping the corresponding points to the global reference frame using the stable transform; generating a list of each point in each 3D point cloud data set and the corresponding points; deriving a second estimated transform that optimally maps each point in each 3D point cloud data set onto the corresponding points; and repeating the steps of generating and deriving a second estimated transform for each 3D point cloud data set using a last derived second estimated transform until the last derived second estimated transform converges to a second stable transform.