摘要
在动态变化的拥挤环境中,移动机器人的传统地图匹配定位算法会由于观测信息的剧烈变化,导致定位性能明显下降甚至完全失效.对此本文提出了一种基于可定位性估计的改进粒子滤波定位算法.本算法一方面借助观测模型的可定位性矩阵估计激光测距仪观测数据的可信度,另一方面通过预测模型的协方差矩阵估计里程计数据的可信度,进而根据这两个指标调节观测信息对预测位姿的修正值.在多种典型走廊环境中,与经典粒子滤波定位算法做了对比实验,结果表明了本文算法对提高复杂环境下移动机器人定位性能的有效性.
In dynamic crowded environments, the localization performance of traditional map-matching algorithms for mobile robot will be significantly decreased, even the localization will completely fail, because of severe changes of the observation information. In this paper, an improved particle filter localization algorithm is proposed based on localizability estimation. On one hand, this algorithm estimates the belief of laser range finder observations using the localizability matrix of observation model. On the other hand, it estimates the belief of the odometer data using the covariance matrix of prediction model. Then based on these two indicators, the predicted robot pose is modified according to the observation information. Experiments of localization and navigation under different typical corridor environments are designed to compare the proposed algorithm with classical particle filter algorithms. The result demonstrates the validity of the proposed localization algorithm under comolex environments.
出处
《机器人》
EI
CSCD
北大核心
2012年第5期596-603,共8页
Robot
基金
国家863计划资助项目(2012AA041403)
国家自然科学基金资助项目(60934006
61175088)
教育部博士点基金资助项目(20100073110018)
机器人技术与系统国家重点实验室开放基金资助项目(SKLRS2011ZD01)
关键词
概率栅格地图
可定位性
粒子滤波
移动机器人
拥挤环境
probabilistic grid map
localizability
particle filter
mobile robot
crowded environment
