ALGORITHM OF PRETREATMENT ON AUTOMOBILE BODY POINT CLOUD  

作　　者：GAO Feng ZHOU Yu DU Farong QU Weiwei XIONG Yonghua 

机构地区：[1]Department of Automobile Engineering, Beihang University, Beijing 100083, China

出　　处：《Chinese Journal of Mechanical Engineering》2007年第4期71-74,共4页中国机械工程学报（英文版）

基　　金：This project is supported by Provincial Technology Cooperation Program of Yunnan,China(No.2003EAAAA00D043).

摘　　要：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.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.

关 键 词：Reverse engineering Point cloud registration Skeleton point Iterative closed point（ICP） Data reduction 

分 类 号：U4[交通运输工程—道路与铁道工程]