Self localization of an autonomous robot: using an EKF to merge odometry and vision based landmarks
Self localization of an autonomous robot: using an EKF to merge odometry and vision based landmarks
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自主机器人的自我定位：使用 EKF 合并里程计和基于视觉的地标
DOI：
10.1109/etfa.2005.1612524
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
A. Carvalho
中科院分区：
文献类型：
--
作者：
Armando Sousa;P. Costa;A. Moreira;A. Carvalho
定位是现代自主机器人在低结构环境中长距离有效完成复杂任务的关键。本文采用扩展卡尔曼滤波实现自定位。这是通过合并里程测量和定位信息（如果可用）来完成的。使用的地标是彩色杆状物，当机器人四处移动执行正常任务时，可以识别这些杆状物。本文将具有不同距离和角度特征的测量物与标记物进行建模，并给出了自定位方法的结果。给出了仿真结果和实际机器人试验结果
Localization is essential to modern autonomous robots in order to enable effective completion of complex tasks over possibly large distances in low structured environments. In this paper, a extended Kalman filter is used in order to implement self-localization. This is done by merging odometry and localization information, when available. The used landmarks are colored poles that can be recognized while the robot moves around performing normal tasks. This paper models measurements with very different characteristics in distance and angle to markers and shows results of the self-localization method. Results of simulations and real robot tests are shown