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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >Pedestrian Simultaneous Localization and Mapping in Multistory Buildings Using Inertial Sensors
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Pedestrian Simultaneous Localization and Mapping in Multistory Buildings Using Inertial Sensors

机译：使用惯性传感器的多层建筑物中的行人同时定位和制图

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Pedestrian navigation is an important ingredient for efficient multimodal transportation, such as guidance within large transportation infrastructures. A requirement is accurate positioning of people in indoor multistory environments. To achieve this, maps of the environment play a very important role. FootSLAM is an algorithm based on the simultaneous localization and mapping (SLAM) principle that relies on human odometry, i.e., measurements of a pedestrian's steps, to build probabilistic maps of human motion for such environments and can be applied using crowdsourcing. In this paper, we extend FootSLAM to multistory buildings following a Bayesian derivation. Our approach employs a particle filter and partitions the map space into a grid of adjacent hexagonal prisms with eight faces. We model the vertical component of the odometry errors using an autoregressive integrated moving average (ARIMA) model and extend the geographic tree-based data structure that efficiently stores the probabilistic map, allowing real-time processing. We present the multistory FootSLAM maps that were created from three data sets collected in different buildings (one large office building and two university buildings). Hereby, the user was only carrying a single foot-mounted inertial measurement unit (IMU). We believe the resulting maps to be strong evidence of the robustness of FootSLAM. This paper raises the future possibility of crowdsourced indoor mapping and accurate navigation using other forms of human odometry, e.g., obtained with the low-cost and nonintrusive sensors of a handheld smartphone.
机译：行人导航是有效的多式联运的重要组成部分，例如大型运输基础设施中的导航。要求是在室内多层环境中准确定位人员。为此，环境地图起着非常重要的作用。 FootSLAM是一种基于同步定位和映射（SLAM）原理的算法，该算法依赖于人类的里程表（即行人的步伐测量）来为此类环境构建人体运动的概率图，并且可以使用众包应用。在本文中，我们根据贝叶斯推导将FootSLAM扩展到多层建筑物。我们的方法采用粒子过滤器，将地图空间划分为具有八个面的相邻六边形棱镜的网格。我们使用自回归综合移动平均（ARIMA）模型对测距误差的垂直分量进行建模，并扩展了基于地理树的数据结构，该结构有效地存储了概率图，从而可以进行实时处理。我们展示了多层FootSLAM地图，这些地图是根据在不同建筑物（一栋大型办公大楼和两栋大学大楼）中收集的三个数据集创建的。因此，用户仅携带一个脚安装式惯性测量单元（IMU）。我们认为，由此产生的地图将成为FootSLAM健壮性的有力证据。本文提出了使用其他形式的人类里程表进行众包的室内地图和精确导航的未来可能性，例如通过手持智能手机的低成本和非侵入式传感器获得的。

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