摘要
通过分析全景视觉与里程计传感器的感知模型的不确定性 ,提出了一种基于路标观测的移动机器人自定位算法 .该算法利用卡尔曼滤波器 ,融合多种传感器在不同观测点获取的观测数据完成机器人自定位 .与传统的、采用单一传感器自定位的方法相比 ,它利用视觉和里程计的互补特性 ,提高了自定位的精度 .实验结果证明了上述方法的有效性 .
By analyzing the uncertainties in perception models of omni-vision and odometer systems for mobile robot, a landmark-observation-based self-localization method with Kalman filter is proposed, which fuses the data from multiple sensors at successive observation points. Compared with single-sensor methods, it exploits the differences in uncertainty between omni-vision and odometer systems, and consequently improves the self-localization precision of mobile robot. The experimental results show the validity and feasibility of the proposed method.
出处
《机器人》
EI
CSCD
北大核心
2005年第1期41-45,共5页
Robot
基金
国家自然科学基金资助项目 ( 6 0 10 50 0 5)
