A human motion model based on maps for navigation systems
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RESEARCH
Open Access
A human motion model based on maps for navigation systems Susanna Kaiser*, Mohammed Khider and Patrick Robertson
Abstract Foot-mounted indoor positioning systems work remarkably well when using additionally the knowledge of floorplans in the localization algorithm. Walls and other structures naturally restrict the motion of pedestrians. No pedestrian can walk through walls or jump from one floor to another when considering a building with different floor-levels. By incorporating known floor-plans in sequential Bayesian estimation processes such as particle filters (PFs), long-term error stability can be achieved as long as the map is sufficiently accurate and the environment sufficiently constraints pedestrians’ motion. In this article, a new motion model based on maps and floor-plans is introduced that is capable of weighting the possible headings of the pedestrian as a function of the local environment. The motion model is derived from a diffusion algorithm that makes use of the principle of a source effusing gas and is used in the weighting step of a PF implementation. The diffusion algorithm is capable of including floor-plans as well as maps with areas of different degrees of accessibility. The motion model more effectively represents the probability density function of possible headings that are restricted by maps and floorplans than a simple binary weighting of particles (i.e., eliminating those that crossed walls and keeping the rest). We will show that the motion model will help for obtaining better performance in critical navigation scenarios where two or more modes may be competing for some of the time (multi-modal scenarios). Keywords: indoor positioning, multi-sensor navigation, particle filtering, human motion models, maps
1 Introduction Indoor navigation is an exciting research and development area that promises new applications for many aspects of our lives. Whereas positioning and navigation outdoor have become ubiquitous and affordable over the last decade or so, providing similar services in indoor environments is extremely challenging. Depending on the required degree of accuracy a number of approaches are being followed [1-3], ranging from high sensitivity GNSS, dedicated wireless systems to inertial navigation as well as various combinations. In this article, we will focus on inertial navigation for pedestrians and the application is continuous and online meterlevel-accuracy positioning with either foot-mounted sensors [4] or other suitable forms of pedestrian dead reckoning (PDR) [5,6]. PDR is based on the principle that we can detect and estimate individual steps of a person. A simple step counter can be used to estimate distance * Correspondence: [email protected] German Aerospace Center (DLR), Institute of Communication and Navigation, 82234 Wessling, Germany
traveled [7] and if we estimate heading changes then we can also estimate the relative location change over time. An advanced form of PDR uses one or more inertial measurement units (IMUs) mounted on suit
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