改进麻雀搜索的点云配准算法

doi:10.3778/j.issn.1002-8331.2402-0202

摘要/Abstract

摘要： 为提高点云配准算法的性能，通常融合群智能优化算法进行前置配准。麻雀搜索算法是基于麻雀群体行为的优化算法。针对其探索效率不足，容易陷入局部最优解等问题提出改进麻雀搜索的点云配准算法。提出TC混沌映射来对麻雀种群初始化，使种群分布更加均匀多样；提出自适应可调参数的非线性探索权重，提高算法的全局搜索和局部探索能力；在种群位置更新中引入精英随机反向学习策略，提高算法的最优解搜索质量；融合二次插值方法对加入者种群更新，进一步优化算法后期解的质量；探索点云初始变换矩阵并结合ICP算法进行精配准。通过多种函数测试和点云配准实验，实验结果表明，改进麻雀搜索的点云配准算法对比多种算法在寻优性能与配准精度上有着显著提升。

关键词: 麻雀算法, 混沌映射, 点云配准, 二次插值

Abstract: In the research, in order to improve the performance of point cloud registration algorithm, swarm intelligence optimization algorithm is usually used for pre-registration. Sparrow search algorithm is an optimization algorithm based on sparrow group behavior. Aiming at the problems of insufficient exploration efficiency and the tendency to easily fall into a local optimal solution, a point cloud registration algorithm based on improved sparrow search is proposed. Firstly, the TC chaotic map is proposed to initialize the sparrow population to make the population distribution more uniform and diverse. Secondly, a nonlinear exploration weight with adaptive adjustable parameters is proposed to improve the global search and local exploration ability of the algorithm. At the same time, the elite random opposition-based learning strategy is introduced in the population position update to improve the quality of the algorithm's optimal solution search. Thirdly, the quadratic interpolation method is integrated to update the population of entrants to further optimize the quality of the solution in the later stage of the algorithm. Finally, the initial transformation matrix of the point cloud is explored and the ICP algorithm is combined for fine registration. Through a variety of function tests and point cloud registration experiments, the results show that the improved sparrow search point cloud registration algorithm has a significant improvement in optimization performance and registration accuracy compared with a variety of algorithms.

Key words: sparrow algorithm, chaotic mapping, point cloud registration, quadratic interpolation

周江伟, 邵洁, 曹盛. 改进麻雀搜索的点云配准算法[J]. 计算机工程与应用, 2025, 61(11): 295-305.

ZHOU Jiangwei, SHAO Jie, CAO Sheng. Point Cloud Registration Algorithm Based on Improved Sparrow Search[J]. Computer Engineering and Applications, 2025, 61(11): 295-305.

参考文献

[1] 王庆闪, 张军, 刘元盛, 等. 基于NDT与ICP结合的点云配准算法[J]. 计算机工程与应用, 2020, 56(7): 88-95.
WANG Q S, ZHANG J, LIU Y S, et al. Point cloud registration algorithm based on combination of NDT and ICP[J]. Computer Engineering and Applications, 2020, 56(7): 88-95.
[2] HE Y, LIANG B, YANG J, et al. An iterative closest points algorithm for registration of 3D laser scanner point clouds with geometric features[J]. Sensors (Basel), 2017, 17(8): 1862.
[3] WU Z Z, CHEN H C, DU S Y, et al. Correntropy based scale ICP algorithm for robust point set registration[J]. Pattern Recognition, 2019, 93: 14-24.
[4] DU S Y, ZHENG N N, YING S H, et al. Affine iterative closest point algorithm for point set registration[J]. Pattern Recognition Letters, 2010, 31(9): 791-799.
[5] FISCHLER M A, BOLLES R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381-395.
[6] MELLADO N, AIGER D, MITRA N J. Super 4PCS fast global pointcloud registration via smart indexing[J]. Computer Graphics Forum, 2014, 33(5): 205-215.
[7] SHI X J, LIU T, HAN X. Improved iterative closest point(ICP) 3D point cloud registration algorithm based on point cloud filtering and adaptive fireworks for coarse registration[J]. International Journal of Remote Sensing, 2020, 41(8): 3197-3220.
[8] 杜沅昊, 耿秀丽, 徐诚智, 等. 基于改进灰狼算法和自适应分裂KD-Tree的点云配准方法[J]. 系统仿真学报, 2025, 37(2): 424-435.
DU Y H, GENG X L, XU C Z, et al. Point cloud registration method based on improved grey wolf algorithm and adaptive splitting KD-Tree [J]. Journal of System Simulation, 2025, 37(2): 424-435.
[9] 王婷, 李凯, 张洁, 等. 三维后期重建中基于果蝇优化的点云数据配准研究[J]. 南京理工大学学报, 2023, 47(5): 692-698.
WANG T, LI K, ZHANG J, et al. Research on point cloud data registration based on fruit fly optimization in 3d post reconstruction[J]. Journal of Nanjing University of Science and Technology, 2023, 47(5): 692-698.
[10] 范怡萍, 葛宝臻, 陈雷. 基于人工蜂群优化的异尺度点云配准算法[J]. 激光与光电子学进展, 2023, 60(12): 236-245.
FAN Y P, GE B Z, CHEN L. Registration algorithm for differently scaled point clouds based on artificial bee colony optimization[J]. Laser & Optoelectronics Progress, 2023, 60(12): 236-245.
[11] LIU H, WANG S L, ZHAO D H. Initial alignment for point cloud registration by improved differential evolution algorithm[J]. OPTIK, 2021, 243: 166856.
[12] 李书群, 陈钰, 杨雨婷, 等. 基于佳点集人工鱼群的点云配准算法[J]. 合肥工业大学学报(自然科学版), 2023, 46(9): 1203-1209.
LI S Q, CHEN Y, YANG Y T, et al. Point cloud registration method based on artificial fish swarm algorithm using good point set[J]. Journal of Hefei University of Technology (Natural Science), 2023, 46(9): 1203-1209.
[13] ZHAN X, CAI Y, LI H, et al. A point cloud registration algorithm based on normal vector and particle swarm optimization[J]. Measurement and Control, 2020, 53(3/4): 265-275.
[14] XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1): 22-34.
[15] 回立川, 张晓泽, 李欢欢. 改进麻雀算法优化支持向量机的接触电阻预测[J]. 电工电能新技术, 2023, 42(8): 60-68.
HUI L C, ZHANG X Z, LI H H. Improved sparrow algorithm for optimizing support vector machines for contact resistance prediction[J]. Advanced Technology of Electrical Engineering and Energy, 2023, 42(8): 60-68.
[16] 戈一航, 杨光永, 于元滐, 等. 基于改进麻雀搜索算法的移动机器人路径规划[J]. 传感器与微系统, 2023, 42(7): 132-135.
GE Y H, YANG G Y, YU Y J, et al. Mobile robot path planning based on improved SSA[J]. Transducer and Microsystem Technologies, 2023, 42(7): 132-135.
[17] ALTAY V E, ALATAS B. Bird swarm algorithms with chaotic mapping[J]. Artificial Intelligence Review, 2020, 53(2): 1373-1414.
[18] 欧阳城添, 周凯. 融合改进天牛须搜索的教与学优化算法[J]. 计算机工程与应用, 2022, 58(4): 91-99.
OUYANG C T, ZHOU K. Teaching-learning based optimization algorithm with improved beetle antennae search[J]. Computer Engineering and Applications, 2022, 58(4): 91-99.
[19] PHATAK S C, RAO S S. Logistic map: a possible random-number generator[J]. Physical Review E, 1995, 51(4): 3670-3678.
[20] SUN C, HE K, XU Q, et al. A novel conservative chaotic circuit with a cubic root[J]. International Journal of Bifurcation and Chaos, 2023, 33(15): 1-15.
[21] POWELL M. Continuity of the Lyapunov exponent for analytic multi-frequency quasiperiodic cocycles[J]. arXiv:2210. 09285, 2022.
[22] 苏莹莹, 王升旭. 自适应混合策略麻雀搜索算法[J]. 计算机工程与应用, 2023, 59(9): 75-85.
SU Y Y, WANG S X. Adaptive hybrid strategy sparrow search algorithm[J]. Computer Engineering and Applications, 2023, 59(9): 75-85.
[23] TENG Z J, LYU J L, GUO L W. An improved hybrid grey wolf optimization algorithm[J]. Soft Computing, 2019, 23(15): 6617-6631.
[24] LI W, YE X Q, HUANG Y, et al. Adaptive dimensional learning with a tolerance framework for the differential evolution algorithm[J]. Complex System Modeling and Simulation, 2022, 2(1): 59-77.
[25] HU G, DU B, LI H N, et al. Quadratic interpolation boosted black widow spider-inspired optimization algorithm with wavelet mutation[J]. Mathematics and Computers in Simulation, 2022, 200: 428-467.
[26] WANG X K, YANG F L, ZHANG H D, et al. Registration of airborne LiDAR bathymetry and multibeam echo sounder point clouds[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 3076462.
[27] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of the International Conference on Neural Networks, 1995: 1942-1948.
[28] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.
[29] 顾嘉城, 龙英文, 吉明明, 等. 基于高斯云改进的混沌麻雀搜索算法与应用[J]. 光电子·激光, 2023, 34(10): 1047-1058.
GU J C, LONG Y W, JI M M, et al. Improved chaotic sparrow search algorithm and application based on Gaussian cloud[J]. Journal of Optoelectronics·Laser, 2023, 34(10): 1047-1058.
[30] 郑旸, 龙英文, 吉明明, 等. 融合螺旋黏菌算法的混沌麻雀搜索算法与应用[J]. 计算机工程与应用, 2023, 59(14): 124-133.
ZHENG Y, LONG Y W, JI M M, et al. Chaotic sparrow search algorithm and application based on spiral slime mould algorithm[J]. Computer Engineering and Applications, 2023, 59(14): 124-133.
[31] AL-BETAR M A, AWADALLAH M A, BRAIK M S, et al. Elk herd optimizer: a novel nature-inspired metaheuristic algorithm[J]. Artificial Intelligence Review, 2024, 57(3): 48.