针对室外环境中基于点特征的同时定位与地图构建(SLAM)算法中存在的计算复杂度与信息丰富度之间的矛盾,提出了提取室外环境点特征并转化为线特征的P-L(Point to Line)地图构建算法.通过连续提取的树木特征点,采取点-线匹配并保存线特征...针对室外环境中基于点特征的同时定位与地图构建(SLAM)算法中存在的计算复杂度与信息丰富度之间的矛盾,提出了提取室外环境点特征并转化为线特征的P-L(Point to Line)地图构建算法.通过连续提取的树木特征点,采取点-线匹配并保存线特征的方法设计地图关联的概率统计方案,将室内环境基于线特征的地图构建方法延伸到室外环境,形成SLAM地图构建中室外环境信息表达的新方法.选用鲁棒滤波算法,在MATLAB实验环境下进行仿真实验,结果表明,采用文中提出的方法可以降低信息表达的复杂度,验证了所提方法的可行性,为进一步室外场地跑车试验奠定了基础.展开更多
This paper presents a novel method, which enhances the use of external mechanisms by considering a multisensor system, composed of sonars and a CCD camera. Monocular vision provides redundant information about the loc...This paper presents a novel method, which enhances the use of external mechanisms by considering a multisensor system, composed of sonars and a CCD camera. Monocular vision provides redundant information about the location of the geometric entities detected by the sonar sensors. To reduce ambiguity significantly, an improved and more detailed sonar model is utilized. Moreover, Hough transform is used to extract features from raw sonar data and vision image. Information is fused at the level of features. This technique significantly improves the reliability and precision of the environment observations used for the simultaneous localization and map building problem for mobile robots. Experimental results validate the favorable performance of this approach.展开更多
文摘针对室外环境中基于点特征的同时定位与地图构建(SLAM)算法中存在的计算复杂度与信息丰富度之间的矛盾,提出了提取室外环境点特征并转化为线特征的P-L(Point to Line)地图构建算法.通过连续提取的树木特征点,采取点-线匹配并保存线特征的方法设计地图关联的概率统计方案,将室内环境基于线特征的地图构建方法延伸到室外环境,形成SLAM地图构建中室外环境信息表达的新方法.选用鲁棒滤波算法,在MATLAB实验环境下进行仿真实验,结果表明,采用文中提出的方法可以降低信息表达的复杂度,验证了所提方法的可行性,为进一步室外场地跑车试验奠定了基础.
基金supported by the National Natural Science Foundation of China (No.60805032)the National High Technology Research and Development Program (No.2006AA040202, 2007AA041703)
文摘This paper presents a novel method, which enhances the use of external mechanisms by considering a multisensor system, composed of sonars and a CCD camera. Monocular vision provides redundant information about the location of the geometric entities detected by the sonar sensors. To reduce ambiguity significantly, an improved and more detailed sonar model is utilized. Moreover, Hough transform is used to extract features from raw sonar data and vision image. Information is fused at the level of features. This technique significantly improves the reliability and precision of the environment observations used for the simultaneous localization and map building problem for mobile robots. Experimental results validate the favorable performance of this approach.
