基于主成分分析和特征图匹配的点云配准方法

doi:10.11959/j.issn.2096-6652.202340

Abstract

Abstract:

Due to varying degrees of overlap in point cloud models, point cloud registration is prone to problems, such as feature matching errors and high difficulty in registration. Therefore, a point cloud registration method based on principal component analysis and feature map matching is proposed. Before registration, the principal component analysis method with spindle correction was used to adjust the initial pose, then the K-dimensional tree was established to search the overlapping area. Secondly, the fast point feature histograms features of the sampling points were calculated according to the overlapping area of the two-point cloud, and the point cloud feature graph matching and trimmed iterative closest point (TrICP) fine registration were performed. Registration experiments were carried out according to the existing datasets and the actual scanning model. The experimental results show that the method has good stability and higher accuracy, and the accuracy can be improved by more than 25% compared with other algorithms.

Key words: overlapping region, K-dimension tree, graph matching, TrICP, point cloud registration

CLC Number:

TP391

Weibin ZHENG, Guofu LIAN, Xueming ZHANG, et al. Point cloud registration method based on principal component analysis and feature map matching[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 543-552.

Figures/Tables 18

点云数据	配准算法	配准误差MSE	时间/s
Bunny	ICP	$11.104 × 10 - 4$	32.1
	文献[8]	$1.922 × 10 - 4$	25.4
	文献[11]	$2.129 × 10 - 4$	19.2
	文献[16]	$2.562 × 10 - 4$	17.9
	FGM+TrICP	$1.816 × 10 - 4$	15.2
Dragon	ICP	$5.165 × 10 - 5$	28.7
	文献[8]	$6.080 × 10 - 5$	22.1
	文献[11]	$4.557 × 10 - 5$	18.9
	文献[16]	$6.921 × 10 - 5$	13.5
	FGM+TrICP	$3.964 × 10 - 5$	14.3
7-Scenes室内场景	ICP	$2.413 × 10 - 3$	42.7
	文献[8]	$3.458 × 10 - 3$	32.7
	文献[11]	$2.556 × 10 - 3$	25.1
	文献[16]	$2.388 × 10 - 3$	22.0
	FGM+TrICP	$2.056 × 10 - 3$	19.8

点云数据	配准算法	配准误差MSE	时间/s
齿轮箱体	ICP	$7.411 × 10 - 4$	39.8
	文献[8]	$4.513 × 10 - 4$	32.7
	文献[11]	$5.253 × 10 - 4$	21.4
	文献[16]	$5.770 × 10 - 4$	16.5
	FGM+TrICP	$2.593 × 10 - 4$	17.2

References 23

1	李博洋, 刘思健, 崔明月, 等. 基于最小回环检测的多车协同SLAM框架[J]. 电子学报, 2021, 49(11): 2241-2250.
	LI B Y, LIU S J, CUI M Y, et al. Multi-vehicle collaborative SLAM framework for minimum loop detection[J]. Acta Electronica Sinica, 2021, 49(11): 2241-2250.
2	DONG Z, LIANG F X, YANG B S, et al. Registration of large-scale terrestrial laser scanner point clouds: a review and benchmark[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 163: 327-342.
3	BESL P J, MCKAY N D. Method for registration of 3-D shapes[C]//Proceedings of SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures. Washington: SPIE, 1992, 1611: 586-606.
4	ZHONG L S, YING J, YANG H M, et al. Triple screening point cloud registration method based on image and geometric features[J]. Optik, 2021, 246: 167763.
5	张旭春, 周洪军, 郑津津, 等. 基于多尺度特征及点距离约束的点云配准[J]. 激光与光电子学进展, 2021, 58(24): 325-333.
	ZHANG X C, ZHOU H J, ZHENG J J, et al. Point cloud registration based on multi-scale features and point distance constraints[J]. Laser & Optoelectronics Progress, 2021, 58(24): 325-333.
6	XU G X, PANG Y J, BAI Z X, et al. A fast point clouds registration algorithm for laser scanners[J]. Applied Sciences, 2021, 11(8): 3426.
7	ZAGANIDIS A, MAGNUSSON M, DUCKETT T, et al. Semantic-assisted 3D normal distributions transform for scan registration in environments with limited structure[C]//Proceedings of 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE Press, 2017: 4064-4069.
8	荆路, 武斌, 李先帅. 基于SAC-IA和NDT融合的点云配准方法[J]. 大地测量与地球动力学, 2021, 41(4): 378-381.
	JING L, WU B, LI X S. Point cloud registration method based on SAC-IA and NDT fusion[J]. Journal of Geodesy and Geodynamics, 2021, 41(4): 378-381.
9	杜辉, 郑长亮, 苗春雨, 等. 一种动态特征匹配的部分重叠点云配准方法[J]. 电信科学, 2021, 37(4): 97-107.
	DU H, ZHENG C L, MIAO C Y, et al. A partial overlapping point cloud registration method based on dynamic feature matching[J]. Telecommunications Science, 2021, 37(4): 97-107.
10	李鑫, 莫思特, 黄华, 等. 自动计算重叠度的多源点云配准方法[J]. 红外与激光工程, 2021, 50(12): 260-268.
	LI X, MO S T, HUANG H, et al. Multi-source point cloud registration method for automatically calculating overlap degree[J]. Infrared and Laser Engineering, 2021, 50(12): 260-268.
11	LI J, QIAN F, CHEN X F. Point cloud registration algorithm based on overlapping region extraction[J]. Journal of Physics: Conference Series, 2020, 1634(1): 012012.
12	张元, 李晓燕, 韩燮. 一种低重叠率的三维点云配准方法[J]. 激光与光电子学进展, 2021, 58(8):162-171.
	ZHANG Y, LI X, HAN X. Three-dimensional point cloud registration method with low overlap rate[J]. Laser & Optoelectronics Progress, 2021, 58(8):162-171.
13	POIESI F, BOSCAINI D. Learning general and distinctive 3D local deep descriptors for point cloud registration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(3): 3979-3985.
14	WANG Y, SOLOMON J. Deep closest point: learning representations for point cloud registration[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2020: 3522-3531.
15	WU Z R, SONG S R, KHOSLA A, et al. 3D ShapeNets: a deep representation for volumetric shapes[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2015: 1912-1920.
16	FU K X, LIU S L, LUO X Y, et al. Robust point cloud registration framework based on deep graph matching[C]//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2021: 8889-8898.
17	HUANG S Y, GOJCIC Z, USVYATSOV M, et al. PREDATOR: registration of 3D point clouds with low overlap[C]//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2021: 4265-4274.
18	CHENG D Y, ZHAO D J, ZHANG J C, et al. PCA-based denoising algorithm for outdoor lidar point cloud data[J]. Sensors, 2021, 21(11): 3703.
19	王来强, 包静. 附加主方向判定的PCA点云数据初始配准算法[J]. 测绘地理信息, 2021, 46(6):59-62, 66.
	WANG L Q, BAO J. Initial registration algorithm for PCA point cloud data with additional main direction decision[J]. Journal of Geomatics, 2021, 46(6): 59-62, 66.
20	LIU J, ZHU J W, YANG J L, et al. Three-dimensional point cloud registration based on ICP algorithm employing K-D tree optimization[C]//Proceedings of Eighth International Conference on Digital Image Processing (ICDIP 2016). Washington: SPIE, 2016, 10033: 872-876.
21	RUSU R B, BLODOW N, BEETZ M. Fast point feature histograms (FPFH) for 3D registration[C]//Proceedings of 2009 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2009: 3212-3217.
22	CHETVERIKOV D, SVIRKO D, STEPANOV D, et al. The trimmed iterative closest point algorithm[C]//Proceedings of 2002 International Conference on Pattern Recognition. Piscataway: IEEE Press, 2002: 545-548.
23	LI J Y, HU Q W, AI M Y. Point cloud registration based on one-point RANSAC and scale-annealing biweight estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(11): 9716-9729.

Metrics

Recommended 0

No Suggested Reading articles found!

模型	重叠率	点数/个	质心位置
Bunny	31%	Q: 40 097	0.0104，0.0984，0.0606
Bunny	31%	P: 30 379	-0.0064，0.1027，0.0064
Dragon	44%	Q: 41 841	-0.0044，0.1157，0.0058
Dragon	44%	P: 22 092	0.0045，0.1145，0.0114
7-Scenes 室内场景	58%	Q: 291 605	0.1772，-0.3775，2.0563
7-Scenes 室内场景	58%	P: 268 376	0.5535，-0.1114，2.0154

点云数据	配准算法	配准误差MSE	时间/s
Bremen city	ICP	7.208	185.7
	文献[8]	1.055	115.6
	文献[11]	0.566	75.3
	文献[16]	1.489	68.6
	FGM+TrICP	0.102	70.2
Wue city	ICP	12.054	192.3
	文献[8]	1.244	105.7
	文献[11]	0.987	77.9
	文献[16]	1.442	69.1
	FGM+TrICP	0.635	67.5

[1]	Wencheng CUI, Keli WANG, Hong SHAO. Abnormal cell segmentation for lung pathological image based on denseblock and attention mechanism [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 525-534.
[2]	Wei TANG, Xiao TAN, Yu SUN, Jiapeng YAN, Guangrui YAN. Research on path planning of material transmission platform based on A* and dynamic window method [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 515-524.
[3]	Zhenqi FANG, Xue LI, Hong MO. Diagnostic of breast tumors based on improved EfficientNet [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 505-514.
[4]	Cong JIN, Jie WANG, Zichun GUO, Jing WANG. Lipsynthesis incorporating audio-visual synchronisation [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(3): 397-405.
[5]	Fengtao XIANG, Jiongming SU, Xueqiang Gu, Wanpeng ZHANG. Research on the spread and countermeasures of COVID-19 using agent-based modeling [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(1): 51-57.
[6]	Zhouyu GU, Yuecheng YU, Tiantian Zhe. Rapider-YOLOX: lightweight object detection network with high precision [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(1): 92-103.
[7]	Xiaofeng CONG, Jie GUI, Jun ZHANG. Underwater image enhancement network based on visual Transformer with multiple loss functions fusion [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(4): 522-532.
[8]	Hang YU, Yanwei FU, Boyan JIANG, Xiangyang XUE. A survey of image-based few-shot 3D reconstruction [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(4): 544-559.
[9]	Yan CHEN, Xueqin LUO, Wei LIANG, Yongfang XIE. Depression recognition based on emotional information fused with attentional mechanism [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(4): 600-609.
[10]	Yongqiang ZHANG, Meilin SONG, Tianhu LIU, Menghua MAN. Research on three frame difference gesture recognition method based on mixed bone features [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(4): 592-599.
[11]	Chao GUO, Yue LU, Xiao WANG, Da YI, Xiao WANG, Fei-Yue WANG. Architecture and key techniques of parallel creation through the fusion of human-cyber-physical intelligence in CPSS [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(3): 344-354.
[12]	Zhou YU, Jing BI, Haitao YUAN. A path planning method for complex naval battle field based on an improved DQN algorithm [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(3): 418-425.
[13]	Renwu LI, Lingxiao ZHANG, Lin GAO, Chunpeng LI, Hao JIANG. Category-level object pose estimation from depth point cloud [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(2): 246-254.
[14]	Mingyang CHEN, Wen ZHANG, Xiangnan CHEN, Hongting ZHOU, Huajun CHEN. Collective knowledge graph: meta knowledge transfer and federated graph reasoning [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(1): 55-64.
[15]	Yue YU, Xin LIU, Fangqing JIANG, Han ZHANG, Hui WANG, Wei ZENG. Exploration of the continual learning ability that supports the application ecological evolution of the large-scale pretraining Peng Cheng series open source models [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(1): 97-108.

Point cloud registration method based on principal component analysis and feature map matching

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 18

References 23

Related Articles 15

Metrics

Recommended 0