里程表
颗粒过滤器
惯性测量装置
计算机科学
火车
地图匹配
人工智能
惯性导航系统
实时计算
滤波器(信号处理)
计算机视觉
全球导航卫星系统应用
概率逻辑
传感器融合
动态贝叶斯网络
全球定位系统
过程(计算)
贝叶斯概率
惯性参考系
电信
量子力学
操作系统
物理
地图学
地理
作者
Oliver Heirich,Patrick Robertson,Adrian Cardalda Garcia,Thomas Strang,Andreas Lehner
标识
DOI:10.1109/ivs.2012.6232194
摘要
The localization of trains in a railway network is necessary for train control or applications such as autonomous train driving or collision avoidance systems. Train localization is safety critical and therefore the approach requires a robust, precise and track selective localization. Satellite navigation systems (GNSS) might be a candidate for this task, but measurement errors and the lack of availability in parts of the railway environment do not fulfill the demands for a safety critical system. Therefore, additional onboard sensors, such as an inertial measurement unit (IMU), odometer and railway feature classification sensors (e.g. camera) are proposed. In this paper we present a top-down train localization approach from theory. We analyze causal dependencies and derive a general Bayesian filter. Furthermore we present a generic algorithm based on particle filter in order to process the multi-sensor data, the train motion and a known track map. The particle filter estimates a topological position directly in the track map without using map matching techniques. First simulations with simplified particular state and measurement models show encouraging results in critical railway scenarios.
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