Point Set Registration for 3D Range Scans Using Fuzzy Cluster-Based Metric and Efficient Global Optimization

被引:29
作者
Liao, Qianfang [1 ]
Sun, Da [1 ]
Andreasson, Henrik [1 ]
机构
[1] Orebro Univ, Ctr Appl Autonomous Sensor Syst AASS, S-70281 Orebro, Sweden
关键词
Measurement; Optimization; Three-dimensional displays; Iterative closest point algorithm; Quality assessment; Robustness; Convergence; Point set registration; fuzzy clusters; registration quality assessment; 3D range scans; branch-and-bound; ROBUST; SYSTEM; ICP;
D O I
10.1109/TPAMI.2020.2978477
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This study presents a new point set registration method to align 3D range scans. In our method, fuzzy clusters are utilized to represent a scan, and the registration of two given scans is realized by minimizing a fuzzy weighted sum of the distances between their fuzzy cluster centers. This fuzzy cluster-based metric has a broad basin of convergence and is robust to noise. Moreover, this metric provides analytic gradients, allowing standard gradient-based algorithms to be applied for optimization. Based on this metric, the outlier issues are addressed. In addition, for the first time in rigid point set registration, a registration quality assessment in the absence of ground truth is provided. Furthermore, given specified rotation and translation spaces, we derive the upper and lower bounds of the fuzzy cluster-based metric and develop a branch-and-bound (BnB)-based optimization scheme, which can globally minimize the metric regardless of the initialization. This optimization scheme is performed in an efficient coarse-to-fine fashion: First, fuzzy clustering is applied to describe each of the two given scans by a small number of fuzzy clusters. Then, a global search, which integrates BnB and gradient-based algorithms, is implemented to achieve a coarse alignment for the two scans. During the global search, the registration quality assessment offers a beneficial stop criterion to detect whether a good result is obtained. Afterwards, a relatively large number of points of the two scans are directly taken as the fuzzy cluster centers, and then, the coarse solution is refined to be an exact alignment using the gradient-based local convergence. Compared to existing counterparts, this optimization scheme makes a large improvement in terms of robustness and efficiency by virtue of the fuzzy cluster-based metric and the registration quality assessment. In the experiments, the registration results of several 3D range scan pairs demonstrate the accuracy and effectiveness of the proposed method, as well as its superiority to state-of-the-art registration approaches.
引用
收藏
页码:3229 / 3246
页数:18
相关论文
共 53 条
[1]  
[Anonymous], 2011, ROBOTIC 3D SCAN REPO
[2]  
[Anonymous], P EUR C MOB ROB ECMR
[3]   PointNetLK: Robust & Efficient Point Cloud Registration using PointNet [J].
Aoki, Yasuhiro ;
Goforth, Hunter ;
Srivatsan, Rangaprasad Arun ;
Lucey, Simon .
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, :7156-7165
[4]   A METHOD FOR REGISTRATION OF 3-D SHAPES [J].
BESL, PJ ;
MCKAY, ND .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1992, 14 (02) :239-256
[5]   FCM - THE FUZZY C-MEANS CLUSTERING-ALGORITHM [J].
BEZDEK, JC ;
EHRLICH, R ;
FULL, W .
COMPUTERS & GEOSCIENCES, 1984, 10 (2-3) :191-203
[6]   Sparse Iterative Closest Point [J].
Bouaziz, Sofien ;
Tagliasacchi, Andrea ;
Pauly, Mark .
COMPUTER GRAPHICS FORUM, 2013, 32 (05) :113-123
[7]   Automatic registration for articulated shapes [J].
Chang, Will ;
Zwicker, Matthias .
COMPUTER GRAPHICS FORUM, 2008, 27 (05) :1459-1468
[8]   OBJECT MODELING BY REGISTRATION OF MULTIPLE RANGE IMAGES [J].
CHEN, Y ;
MEDIONI, G .
IMAGE AND VISION COMPUTING, 1992, 10 (03) :145-155
[9]   Robust euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm [J].
Chetverikov, D ;
Stepanov, D ;
Krsek, P .
IMAGE AND VISION COMPUTING, 2005, 23 (03) :299-309
[10]   3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder [J].
Elbaz, Gil ;
Avraham, Tamar ;
Fischer, Anath .
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, :2472-2481