期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于粒子群优化的机器人多传感器自标定方法 被引量：10

A Robot Multi-sensor Self-calibration Method Based on Particle Swarm Optimization
在线阅读 下载PDF
导出
摘要 针对机器人感知系统中多个传感器的位姿关系估计问题,提出一种新的基于粒子群优化的自标定方法.利用激光扫描点在图像上的投影线约束条件,建立摄像机与激光扫描仪外参数的非线性优化目标函数,采用粒子群算法优化外参数.该方法对参数的初值并不敏感,无需特定的标定物.实验结果表明,该算法的标定精度高. Aiming at the estimation problem of the position and orientation relationship of multiple sensors in robotic perceptual system, a new self-calibration approach based on particle swarm optimization （PSO） is proposed. According to the constraint of the projection line produced by laser scanning points in image, the nonlinear optimization objective function of the extrinsic parameters between a camera and a laser scanner is established. And particle swarm optimization method is adopted for extrinsic parameters. The approach is insensitive to the initial value of parameters, and does not require any calibration object as well. The experimental results confirm that the proposed algorithm can yield high accurate calibration results.
作者 汪霖 曹建福 韩崇昭
机构地区 西安交通大学电子与信息工程学院
出处 《机器人》 EI CSCD 北大核心 2009年第5期391-396,共6页 Robot
基金 国家863计划资助项目(2006AA01Z126)
关键词 粒子群优化 摄像机 激光扫描仪 自标定 particle swarm optimization camera laser scanner self-calibration
分类号 TP24 [自动化与计算机技术—检测技术与自动化装置]
  • 相关文献

参考文献11

  • 1Zhang Q L, Pless R. Extrinsic calibration of a camera and laser range finder (improves camera calibration)[C]//Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ, USA: IEEE, 2004: 2301-2306.
  • 2Li G H, Liu Y H, DongL, et al. An algorithm for extrinsic parameters calibration of a camera and a laser range finder using line features[C]//Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ, USA: IEEE, 2007: 3854-3859.
  • 3Scaramuzza D, Harati A, Siegwart R. Extrinsic self calibration of a camera and a 3D laser range finder from natural scenes[C]//Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ, USA: IEEE, 2007: 4164-4169.
  • 4Tsai R Y. A versatile camera calibration technique for highaccuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses[J]. IEEE Journal of Robotics and Automation, 1987, 3(4): 323-344.
  • 5Hartley R, Zisserman A. Multiple view geometry in computer vision[M]. 2nd ed. Cambridge, UK: Cambridge University Press, 2004: 158-166.
  • 6Kennedy J, Eberhart R C. Particle swarm optimization[C]// Proceedings of IEEE International Conference on Neural Networks. Piscataway, NJ, USA: IEEE, 1995: 1942-1948.
  • 7van den Bergh E Engelbrecht A E A cooperative approach to particle swarm optimization[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 225-239.
  • 8Laskari E C, Parsopoulos K E, Vrahatis M N. Particle swarm optimization for integer programming[C]//Proeeedings of the IEEE Congress on Evolutionary Computation. Piseataway, NJ, USA: IEEE, 2002: 1582-1587.
  • 9Shi Y, Eberhart R. A modified particle swarm optimizer[C]// Proceedings of the IEEE International Conference on Evolutionary Computation. Piscataway, NJ, USA: IEEE, 1998: 69- 73.
  • 10谢晓锋,张文俊,杨之廉.微粒群算法综述[J].控制与决策,2003,18(2):129-134. 被引量：424

二级参考文献34

  • 1[31]Eberhart R, Hu Xiaohui. Human tremor analysis using particle swarm optimization[A]. Proc of the Congress on Evolutionary Computation[C].Washington,1999.1927-1930.
  • 2[32]Yoshida H, Kawata K, Fukuyama Y, et al. A particle swarm optimization for reactive power and voltage control considering voltage security assessment[J]. Trans of the Institute of Electrical Engineers ofJapan,1999,119-B(12):1462-1469.
  • 3[33]Eberhart R, Shi Yuhui. Tracking and optimizing dynamic systems with particle swarms[A]. Proc IEEE Int Conf on Evolutionary Computation[C].Hawaii,2001.94-100.
  • 4[34]Prigogine I. Order through Fluctuation: Self-organization and Social System[M]. London: Addison-Wesley,1976.
  • 5[1]Kennedy J, Eberhart R. Particle swarm optimization[A]. Proc IEEE Int Conf on Neural Networks[C].Perth,1995.1942-1948.
  • 6[2]Eberhart R, Kennedy J. A new optimizer using particle swarm theory[A]. Proc 6th Int Symposium on Micro Machine and Human Science[C].Nagoya,1995.39-43.
  • 7[3]Millonas M M. Swarms Phase Transition and Collective Intelligence[M]. MA: Addison Wesley, 1994.
  • 8[4]Wilson E O. Sociobiology: The New Synthesis[M]. MA: Belknap Press,1975.
  • 9[5]Shi Yuhui, Eberhart R. A modified particle swarm optimizer[A]. Proc IEEE Int Conf on Evolutionary Computation[C].Anchorage,1998.69-73.
  • 10[6]Kennedy J. The particle swarm: Social adaptation of knowledge[A]. Proc IEEE Int Conf on Evolutionary Computation[C].Indiamapolis,1997.303-308.

共引文献423

同被引文献109

引证文献10

二级引证文献99

机器人
2009年 第5期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部