Abstract：Scattered point pattern matching is an important issue in computer vision and pattern recognition, which is widely used for target recognition, medical and remote sensing image registration, and position and pose measurements. This paper describes an algorithm for use in an explosive ordnance disposal robot for when the target object is partial exposed. A simulated annealing-particle swarm optimization (SA-PSO) algorithm based on particle density distribution changes for scattered point pattern matching is developed. The algorithm is more accurate and faster than previous algorithms. The fitness function is constructed from a series of scattered points collected from the local surface of a rotated object. Accurate surface fitting and pose parameters are found using coordinate transforms. The factors that affect the accuracy and applicability are discussed in detail. Tests show that this method is accurate and efficient, with less points needed and less sensitivity to point errors compared with the traditional least squares method.
罗磊, 陈恳, 杜峰坡, 马振书. 基于改进型粒子群算法的曲面匹配与位姿获取[J]. 清华大学学报（自然科学版）, 2015, 55(10): 1061-1066.
LUO Lei, CHEN Ken, DU Fengpo, MA Zhenshu. Surface fitting and position-pose measurements based on an improved SA-PSO algorithm. Journal of Tsinghua University(Science and Technology), 2015, 55(10): 1061-1066.
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2015, Vol. 55 
