摘要
为提高机器人在移动路径中对道路坡度的估计精度,提出一种面向应用的RGB-D(red green blue-depth)机器人融合型道路坡度估计方法。首先,引入随机采样一致性算法完成点云处理;其次,采用改进型平面拟合方法完成法向量估计;最后,采用余弦聚类及累加平均方法实现高精度道路坡度估计。实验结果表明,该算法在数据集下相较最小二乘法与稀疏子空间法,估计误差分别降低1.21%、2.13%,在实际环境下较最小二乘法平均误差降低1.43°,这证明了所提方法的可行性和准确性。
In order to improve the estimation accuracy of the road slope during the movement of the robot,this paper proposed a fusion slope estimation algorithm for RGB-D moving robot.Firstly,the method used random sampling consistency algorithm to complete the point cloud processing.Secondly,the normal vector estimation followed an improve plane fitting method.Finally,it used the cosine clustering and cumulative average method to accurately complete the road slope estimation.Experimental results show that compared with the least squares method and the sparse subspace method under the data set,the estimation error of the algorithm is reduced by 1.21%and 2.13%respectively,in the actual environment,the average error is reduced by 1.43°compared with the least squares method,which verifies the feasibility and effectiveness of the proposed algorithm.
作者
凌晨飞
党淑雯
陈丽
Ling Chenfei;Dang Shuwen;Chen Li(School of Air Transport,Shanghai University of Engineering Science,Shanghai 201600,China)
出处
《计算机应用研究》
CSCD
北大核心
2022年第10期3095-3100,共6页
Application Research of Computers
基金
国家自然科学基金资助项目(52175103)。
