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.
Pedestrian Simultaneous Localization and Mapping in Multistory Buildings Using Inertial Sensors
IEEE Transactions on Intelligent Transportation Systems ; 15 , 4 ; 1714-1727
2014-08-01
1894786 byte
Article (Journal)
Electronic Resource
English
Pedestrian Simultaneous Localization and Mapping in Multistory Buildings Using Inertial Sensors
Online Contents | 2014
|Collaborative Pedestrian Mapping of Buildings Using Inertial Sensors and FootSLAM
British Library Conference Proceedings | 2011
|Methods for Localization and Mapping Using Vision and Inertial Sensors
British Library Conference Proceedings | 2008
|Managing Large-Scale Mapping and Localization for Pedestrians using Inertial Sensors
German Aerospace Center (DLR) | 2013
|