Robust localization algorithms for an autonomous campus tour guide
Robust localization algorithms for an autonomous campus tour guide
复制标题
适用于自主校园导游的强大定位算法
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
D. Subramanian
中科院分区：
文献类型：
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作者：
Richard Thrapp;Christian Westbrook;D. Subramanian
本文描述了一种用于莱斯大学校园漫游的户外机器人的鲁棒定位方法。该机器人采用扩展卡尔曼滤波融合里程计和GPS数据。我们提出并实验测试了一种技术，用于处理两种类型的GPS数据质量非平稳性：由卫星视线访问突然障碍物引起的GPS位置读数的突然变化，以及由大气条件差异引起的更渐进的变化。我们构建了以可见卫星数量为索引的测量误差协方差矩阵，并将其自动转换到定位计算中。该矩阵通过沿路线重复采样GPS数据来建立，并不断更新以处理GPS数据质量的漂移。我们证明了我们的方法比仅使用单个误差协方差矩阵的扩展卡尔曼滤波器性能更好。通过一个精度为1米的GPS接收器，我们已经能够通过莱斯大学工程四边形的一条具有挑战性的路线定位到40厘米。
This paper describes a robust localization method for an outdoor robot that gives tours of the Rice University campus. The robot fuses odometry and GPS data using extended Kalman filtering. We propose and experimentally test a technique for handling two types of nonstationarity in GPS data quality: abrupt changes in GPS position readings caused by sudden obstructions to line of sight access to satellites, and more gradual changes caused by disparities in atmospheric conditions. We construct measurement error covariance matrices indexed by number of visible satellites and switch them into the localization computation automatically. The matrices are built by sampling GPS data repeatedly along the route and are updated continuously to handle drift in GPS data quality. We demonstrate that our approach performs better than extended Kalman filters that use only a single error covariance matrix. With a GPS receiver that delivers 1 meter accuracy, we have been able to localize to 40 cm through a challenging route in the Engineering Quadrangle of Rice University.