基于色彩信息的自适应进化点云拼接算法
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摘要
针对现有进化点云拼接算法未使用点云色彩信息的局限性,提出一种基于色彩信息的自适应进化点云拼接算法。使用随机采样与色彩特征点相结合的方式对输入点云进行采样,通过最小化包含色彩约束的点对距离中值建立目标函数,利用自适应进化算法求解两片点云间的最优空间变换,实现点云的有效拼接。对四幅彩色点云进行拼接实验,结果表明,与仅使用空间信息的自适应进化点云拼接算法和其他两种较新的进化拼接算法相比,所提算法在保证同等拼接精度的情况下能够有效缩短拼接时间。
Traditional evolutionary point cloud registration methods often not using the color information in the models. To overcome the defect,this paper introduced a point cloud registration method based on self-adaptive evolutionary optimization algorithm and color information. It subsampled the input point clouds by extracting the color feature points and randomly chose points,it utilized the median of all pairs of color constrained points as the object function. At last,it used the self-adaptive evolutionary optimization algorithm to get optimal solution. The registration experiments on four colorized point clouds show that,compared with the evolutionary point cloud registration methods only spatial information use in and two state-of-the-art registration methods,the method significantly shorten the processing time while achieving similar registration precision.
Traditional evolutionary point cloud registration methods often not using the color information in the models. To overcome the defect,this paper introduced a point cloud registration method based on self-adaptive evolutionary optimization algorithm and color information. It subsampled the input point clouds by extracting the color feature points and randomly chose points,it utilized the median of all pairs of color constrained points as the object function. At last,it used the self-adaptive evolutionary optimization algorithm to get optimal solution. The registration experiments on four colorized point clouds show that,compared with the evolutionary point cloud registration methods only spatial information use in and two state-of-the-art registration methods,the method significantly shorten the processing time while achieving similar registration precision.
引文
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[24] Men Hao,Pochiraju K. Hue-assisted automatic registration of color point clouds[J]. Journal of Computational Design&Engineering,2014,1(4):223-232.
[25] Lai K,Bo Liefeng,Ren Xiaofeng,et al. A large-scale hierarchical multi-view RGB-D object dataset[C]//Proc of IEEE International Conference on Robotics and Automation. Piscataway,NJ:IEEE Press,2011:1817-1824.
[26]Falco I D,Cioppa A D,Maisto D,et al. Using an adaptive invasionbased model for fast range image registration[C]//Proc of the Conference on Genetic and Evolutionary Computation. New York:ACM Press,2014:1095-1102.
[2] Besl P J,Mc Kay N D. A method for registration of 3-D shapes[J].IEEE Trans on Pattern Analysis and Machine Intelligence,1992,14(2):239-256.
[3] Li Taifeng,Gao Liang,Pan Quanke,et al. Differential evolution algorithm-based range image registration with scaling parameters[C]//Proc of IEEE International Conference on Image Processing. Piscataway,NJ:IEEE Press,2016:4508-4512.
[4] Bermejo E,Cordón O,Damas S,et al. A comparative study on the application of advanced bacterial foraging models to image registration[J]. Information Sciences,2015,295(2):160-181.
[5] García-Torres J M,Damas S,Cordón O,et al. A case study of innovative population-based algorithms in 3D modeling:artificial bee colony,biogeography-based optimization,harmony search[J]. Expert Systems with Applications,2014,41(4):1750-1762.
[6] Domeniconi S,Solerio L. Genetic algorithms for voxel-based medical image registration[C]//Proc of the 4th IEEE International Workshop on Computational Intelligence in Medical Imaging. Piscataway,NJ:IEEE Press,2013:22-29.
[7] Falco I D,Cioppa A D,Maisto D,et al. Differential evolution as a viable tool for satellite image registration[J]. Applied Soft Computing,2008,8(4):1453-1462.
[8]葛宝臻,周天宇,陈雷,等.基于改进ISS特征点与人工蜂群算法的点云拼接方法[J].天津大学学报:自然科学与工程技术版,2016,49(12):1296-1302.(Ge Baozhen,Zhou Tianyu,Chen Lei,et al. Point clouds registration algorithm based on improved ISS feature points and artificial bee colony algorithm[J]. Journal of Tianjin University:Science and Technology,2016,49(12):1296-1302.)
[9]高鹏东,彭翔,李阿蒙,等.基于混合遗传算法和点面距离测度的深度像配准[J].计算机应用研究,2007,24(12):354-356,360.(Gao Pengdong,Peng Xiang,Li Ameng,et al. Range image registration using hybrid genetic algorithm and point-to-plane distance-based measure[J]. Application Research of Computers,2007,24(12):354-356,360.)
[10]Khoshelham K,Elberink S O. Accuracy and resolution of Kinect depth data for indoor mapping applications[J]. Sensors,2012,12(2):1437-54.
[11]Nguyen C V,Izadi S,Lovell D. Modeling Kinect sensor noise for improved 3D reconstruction and tracking[C]//Proc of the 2nd IEEE International Conference on 3D Imaging,Modeling,Processing,Visualization&Transmission. Piscataway,NJ:IEEE Press,2012:524-530.
[12]陈晓明,蒋乐天,应忍冬.基于Kinect深度信息的实时三维重建和滤波算法研究[J].计算机应用研究,2013,30(4):1216-1218.(Chen Xiaoming,Jiang Tianle,Ying Rendong. Research of 3D reconstruction and filtering algorithm based on depth information of Kinect[J].Application Research of Computers,2013,30(4):1216-1218.)
[13]Chow K C,Tsui H T,Lee T. Surface registration using a dynamic genetic algorithm[J]. Pattern Recognition,2004,37(1):105-117.
[14]Santamaría J,Damas S,Cordón O,et al. Self-adaptive evolution toward new parameter free image registration methods[J]. IEEE Trans on Evolutionary Computation,2013,17(4):545-557.
[15]Storn R,Price K. Differential evolution:a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization,1997,11(4):341-359.
[16] Hansen P,Mtadenovic N. Variable neighborhood search[J]. Computers in Operations Research,1997,24(11):1097-1100.
[17]Dasgupta D,Dasgupta D. Artificial immune systems and their applications[M]. Berlin:Springer,1999:121-124.
[18]Zhang Zhengyou. Iterative point matching for registration of free-form curves and surfaces[J]. International Journal of Computer Vision,1994,13(2):119-152.
[19]Yamany S M,Ahmed M N,Hemayed E E,et al. Novel surface registration using the grid closest point(GCP)transform[C]//Proc of International Conference on Image Processing. Piscataway,NJ:IEEE Press,1998:809-813.
[20] Santamaría J,Cordón O,Damas S,et al. Performance evaluation of memetic approaches in 3D reconstruction of forensic objects[J]. Soft Computing,2009,13(8):883-904.
[21] Silva L,Bellon O R,Boyer K L. Precision range image registration usinga robust surface interpenetration measure and enhanced genetic algorithms[J]. IEEE Trans on Pattern Analysis&Machine Intelligence,2005,27(5):762-776.
[22] Wachowiak M P,Smolikova R,Zheng Yufeng,et al. An approach to multimodal biomedical image registration utilizing particle swarm optimization[J]. IEEE Trans on Evolutionary Computation,2004,8(3):289-301.
[23]Men Hao,Gebre B,Pochiraju K. Color point cloud registration with 4D ICP algorithm[C]//Proc of IEEE International Conference on Robotics and Automation. Piscataway,NJ:IEEE Press,2011:1511-1516.
[24] Men Hao,Pochiraju K. Hue-assisted automatic registration of color point clouds[J]. Journal of Computational Design&Engineering,2014,1(4):223-232.
[25] Lai K,Bo Liefeng,Ren Xiaofeng,et al. A large-scale hierarchical multi-view RGB-D object dataset[C]//Proc of IEEE International Conference on Robotics and Automation. Piscataway,NJ:IEEE Press,2011:1817-1824.
[26]Falco I D,Cioppa A D,Maisto D,et al. Using an adaptive invasionbased model for fast range image registration[C]//Proc of the Conference on Genetic and Evolutionary Computation. New York:ACM Press,2014:1095-1102.