摘要
提出采用超声波距离扫描传感器和视觉传感器数据融合技术实现室内环境复杂特征角和半平面的提取,以便更精确地重构环境特征.利用与坐标无关的对称扰动模型建立超声波扫描的环境特征模型、改进的扩展卡尔曼滤波估计求解.以马氏距离作为特征融合判定的依据,并且在传感器校准时,采用基于局部强度和消逝线的摄像机自动校准方法,提高了水平边界点的校准精度,从而使得角的精确度得到大幅改善,较为精确的二维多边形环境地图得以重建,为最终实现机器人的准确定位奠定了基础.
Based on the multi-sensor data fusion technology, a robot with ultrasonic rangefinder and CCD camera is proposed to extract the complex characteristic corners and semiplanes in an indoor environment, so as to rebuild the relevant environmental features more accurately. Environmental features observed by ultrasonic scan are defined with symmetrical perturbation model that is regardless of coordinates, and the product of their bound matrix and relative location vector is considered as the fusion observation equation. EKF （extended Kalman filter） is used to estimate the solution to the equation with Mahalanobis distance taken as the criterion for characteristic fusion. In addition, the CCD automatic calibration based on local intensity and vanishing line is introduced to improve the calibration accuracy of horizontal boundary points, thus enhancing greatly the corner accuracy. In such a way the 2D polynomial environmental map can be replotted to lay down a solid foundation for robot localization. n
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第2期161-164,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(60475036)
国家教育部博士点基金资助项目(20040145012)
关键词
数据融合
对称扰动模型
摄像机自动校准
多边形环境地图
机器人定位
data fusion
symmetrical perturbation model
CCD automatic calibration
polynomial environmental map
robot localizatio
