文献类型：期刊文献

英文题名：A factor graph optimization mapping based on normal distributions transform

作者：Zhong, Kedi[1];Liu, Yuansheng[2];Yang, Jiansuo[2];Lu, Ming[3];Zhang, Jun[2]

第一作者：Zhong, Kedi

通讯作者：Liu, YS[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing, Peoples R China;[3]Beijing Union Univ, Coll Appl Sci & Technol, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2022

卷号：30

期号：3

起止页码：1127-1141

外文期刊名：TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES

收录：;EI(收录号：20221611981771);Scopus(收录号：2-s2.0-85128277189);WOS:【SCI-EXPANDED(收录号:WOS:000774599800039)】；

基金：This work was supported by the Premium Funding Project for Academic Human Resources Development in Beijing Union University under Grant No. BPHR2020BZ01, the National Natural Science Foundation of China under Grant No. 61871039 and No. 61871038, the Beijing Union University Graduate Research and Innovation Funding Project under Grant No. YZ2020K001, and the Science and Technique General Program of Beijing Municipal Commission of Education under Grant No. KM202011417001.

语种：英文

外文关键词：NDT; factor graph optimization; sliding window; point cloud map

摘要：This paper aims to achieve highly accurate mapping results and real time pose estimation of autonomous vehicle by using the normal distribution transform (NDT) algoritm. A factor graph optimization algorithm (FGO-NDT) is proposed to address the poor real-time performance and pose drift errors of the NDT algorithm. Smooth point cloud data are obtained by multisensor calibration and data preprocessing. NDT registration is then used for lidar odometry and feature matching. The global navigation satellite system (GNSS) data and loop detection results are added to the factor graph framework as the pose constraint factors to optimize the pose trajectory and eliminate the pose drift error generated during mapping. In addition, a sliding window method is used for map registration to extract a local map to shorten the map loading time. Thus, the real-time performance of creating point cloud maps of large scenes is significantly improved. Several experiments are conducted in different environments to verify the accuracy and performance of the FGO-NDT. The experimental results demonstrate that the proposed method can eliminate the pose estimation error caused by drift, improve the local structure, and reduce and root mean square error.