摘要
自主机器人定位中,从激光雷达扫描数据提取出精确的环境特征将能大幅提高即时定位与构图(Simultaneous localization and mapping,SLAM)技术中匹配的速度。目前,特征提取算法大多采用迭代计算的方法,计算复杂度高,计算量较大。针对此问题,提出了一种角点特征的提取方法。该方法避免了迭代计算,通过角点定位对分割结果进行修正,在保证精度的前提下,使用两点拟合直线代替了最小二乘法。首先,使用激光雷达获得的扫描点对应矢径长度和角度,计算相邻点的斜率差,对点集进行初始分割。然后,计算分割后每部分点集对应线段的斜率,对过分割的点集进行合并。最后,通过计算相邻两直线的交点对角点特征进行定位和提取。通过实验验证,该算法能够准确地提取出数据帧中的角点特征,并且具有较好的位置精度和计算效率。
In localization problems of autonomous robots,if accurate environmental features can be extracted from the scanning data of laser radar,the matching speed in simultaneous localization and mapping(SLAM)will be greatly improved.At present,most approaches for feature extraction adopt the iteration strategy,which have high computational complexity.To overcome these drawbacks,a new algorithm is proposed to extract corner feature.In the method,iteration is avoided,and on the premise of ensuring the accuracy,the least square method is replaced by two-point fitting line by positioning the corner points to modify the segmentation result.First,the length and angle of the scanning points obtained from the laser radar are used to calculate the slope difference of the adjacent points for the initial segmentation of the point set.Then,after calculating the slopes of the line segments corresponding to each point set,the point set is merged to solve the over-segmentation problem.Finally,the corner feature is located and extracted by calculating the intersection point of two adjacent lines.Experimental results show that the developed algorithm can extract corner features from the scanning data accurately and has better position accuracy and computational efficiency.
作者
刘朋
任工昌
何舟
LIU Peng;REN Gongchang;HE Zhou(College of Mechanical and Electrical Engineering,Shaanxi University of Science and Technology,Xi’an 710021,China)
出处
《南京航空航天大学学报》
EI
CAS
CSCD
北大核心
2021年第3期366-372,共7页
Journal of Nanjing University of Aeronautics & Astronautics
基金
国家自然科学基金(61803246)资助项目。
关键词
自主机器人
特征提取
角点
激光雷达
即时定位与构图
autonomous robot
feature extraction
corner point
laser radar
simultaneous localization and mapping(SLAM)
