摘要
介绍了一种基于2维激光测距仪的自然路标特征提取方法.本方法由3部分组成:数据聚类、滤波平滑以及特征提取.该方法根据计算机图形学中的尺度空间理论,利用无迹卡尔曼滤波器(unscented Kalman filter)构建曲线模型估计器,对描述环境局部区域形状的扫描点间的几何拓扑关系进行估计.根据估计过程中获得的Mahalanobis距离构建滤波卷积核对原始范围图像进行自适应滤波平滑处理,利用距离数据的曲率函数对聚类后的数据进行分割并提取特征.该方法对噪声鲁棒稳定,并且可以有效地提取非结构化环境中的自然路标特征.实验结果证明本文所提出的方法对自然路标特征提取是行之有效的,可以为自主移动机器人导航系统提供丰富的路标特征.
A natural landmark extraction method based on 2D laser rangefinder is described.The framework consists of three main parts： data clustering,filtering and feature extraction.According to the scale space theory in computer graphics,a curve-based estimator is developed using UKF（unscented Kalman filter）,and the scan point topology describing the local environment is estimated.The filtering convolution kernel is constructed with the Mahalanobis distances obtained during estimation,and adaptive filtering of the original range image is achieved.Clustered data is segmented and characterized by the curvature function of the range data.This method is robust to noise,and can reliably extract natural landmarks in unstructured environments.Experimental results show that the proposed method is efficient in natural-landmark extraction,which can provide plenty landmarks for navigation system of autonomous mobile robot.
出处
《机器人》
EI
CSCD
北大核心
2010年第4期540-546,共7页
Robot
基金
国家863计划资助项目(2007AA041604)
上海市科委重点项目(07DZ05805)
上海大学博士生创新基金资助项目(A.16-0109-09-702)
关键词
自然路标提取
曲线估计
自适应滤波
尺度空间
移动机器人
natural landmark extraction
curve estimator
adaptive filtering
scale space
mobile robot
