摘要
提出一种新的基于局部描述符的点云物体识别算法。算法根据点云的位置信息提取出邻域以及曲率信息,进而得到形状索引信息。根据形状索引提取到特征点,在每个特征点根据样条拟合原理得到测地距离和矢量夹角分割曲面得到曲面片集。每个曲面片的等距测地线构成了曲面片指纹,通过矢量和半径的变化描述,可以把每个模型物体得到的曲面片集描述存入数据库。对于给定的一个物体,根据上面步骤同样得到其曲面片集描述,通过和数据库中模型物体曲面片集的比对,得到初始识别结果。对每对初始识别结果进行对应滤波后,通过最近点迭代方法得到最终的识别结果。最后通过具体的实验说明了算法的有效性和高效性。
A 3D points cloud object recognition algorithm was proposed based on local descriptor. The vector and shape index value of the points cloud were calculated, and then according to the shape index, feaiure points were extracted. Through the geodesic distance and vector angle, points cloud was segmented into different patches centered on feature points. Through the geodesic partition on each patch, 3D geodesic-style concentric circles were got. Thus the description of 3D objects could be transformed into two 2D curves： the normal vector curves and the Euclidean distance curves, then one model objects database would be established. Through comparison of the descriptions with the model database, some potential recognition results of a given object could be found. With the final iterative closest point algorithm, the final recognition result could be determined. The experimental results in real objects demonstrate the effectiveness of the proposed algorithm.
出处
《计算机应用》
CSCD
北大核心
2010年第10期2718-2722,共5页
journal of Computer Applications
基金
中国民航飞行学院博士启动基金资助项目(J2009-45)
关键词
物体识别
点云
测地线
指纹
局部描述符
object recognition
points cloud
geodesic curve
fingerprint
local descriptor
