TY - JOUR
T1 - Algorithm of pretreatment on automobile body point cloud
AU - Gao, Feng
AU - Zhou, Yu
AU - Du, Farong
AU - Qu, Weiwei
AU - Xiong, Yonghua
PY - 2007/8
Y1 - 2007/8
N2 - As point cloud of one whole vehicle body has the traits of large geometric dimension, huge data and rigorous reverse precision, one pretreatment algorithm on automobile body point cloud is put forward. The basic idea of the registration algorithm based on the skeleton points is to construct the skeleton points of the whole vehicle model and the mark points of the separate point cloud, to search the mapped relationship between skeleton points and mark points using congruence triangle method and to match the whole vehicle point cloud using the improved iterative closed point (ICP) algorithm. The data reduction algorithm, based on average square root of distance, condenses data by three steps, computing datasets' average square root of distance in sampling cube grid, sorting order according to the value computed from the first step, choosing sampling percentage. The accuracy of the two algorithms above is proved by a registration and reduction example of whole vehicle point cloud of a certain light truck.
AB - As point cloud of one whole vehicle body has the traits of large geometric dimension, huge data and rigorous reverse precision, one pretreatment algorithm on automobile body point cloud is put forward. The basic idea of the registration algorithm based on the skeleton points is to construct the skeleton points of the whole vehicle model and the mark points of the separate point cloud, to search the mapped relationship between skeleton points and mark points using congruence triangle method and to match the whole vehicle point cloud using the improved iterative closed point (ICP) algorithm. The data reduction algorithm, based on average square root of distance, condenses data by three steps, computing datasets' average square root of distance in sampling cube grid, sorting order according to the value computed from the first step, choosing sampling percentage. The accuracy of the two algorithms above is proved by a registration and reduction example of whole vehicle point cloud of a certain light truck.
KW - Data reduction
KW - Iterative closed point(ICP)
KW - Point cloud registration
KW - Reverse engineering
KW - Skeleton point
UR - https://www.scopus.com/pages/publications/34548476485
U2 - 10.3901/CJME.2007.04.071
DO - 10.3901/CJME.2007.04.071
M3 - 文章
AN - SCOPUS:34548476485
SN - 1000-9345
VL - 20
SP - 71
EP - 74
JO - Chinese Journal of Mechanical Engineering (English Edition)
JF - Chinese Journal of Mechanical Engineering (English Edition)
IS - 4
ER -