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Pedestrian positioning is still a challenging field of interest when not receiving any GNSS or other reference signals as it might be the case in indoor environments or tunnels. For professional applications, where it is not possible to rely on any infrastructure, a common technique is to mount Inertial Measurement Units (IMUs) on the foot or other parts of the body for positioning. IMU based techniques...
In the field of indoor navigation, inertial measurement units (IMUs) are commonly used to track pedestrians. Unfortunately, in conjunction with dead reckoning localization approaches, the position accuracy is degraded by the accumulation of sensor errors. Most pedestrian dead reckoning systems are therefore adapted to human gait patterns to constraint this error growth. These interventions however...
The aim of this paper is to compare two inertial navigation systems in order to identify their strengths and weaknesses as well as to propose new fusion algorithms for these systems. The goal of the fusion is to combine the best of both navigation systems in order to obtain an improved position estimation of the pedestrian. To that extent, the comparison starts with an analysis of the sensor parameters...
Infrastructureless indoor navigation remains a challenging research area in spite of the fact that multiple low cost sensors suitable for positioning recently became available even in mobile phones. Since Global Navigation Satellite Signals (GNSS) are often unavailable indoors, the use of Inertial Measurement Units (IMUs) seems to be promising for indoor navigation. Auspicious results are available...
FootSLAM (Simultaneous Localization and Mapping) is a new technology that addresses the indoor mapping and positioning challenge that relies only on sensors that the person carries. In this paper, we propose to use maps-based angular Probability Density Functions (PDFs), using prior knowledge of the building layout as prior maps for FootSLAM and show how they can be integrated into the FootSLAM weight...
By incorporating known floor-plans in sequential Bayesian positioning estimators such as Particle Filters (PF), long term positioning accuracy can be achieved as long as the map is sufficiently accurate and the environment sufficiently constraints pedestrians' motion. Instead of using binary decisions to eliminate particles when crossing a wall as several authors do, a maps-based angular probability...
Foot-mounted indoor positioning systems work remarkably well when using additionally the knowledge of floor-plans 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...
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