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Toni
2016-02-29 12:03:57 +01:00
parent 6ef06459cb
commit cabf60c851
5 changed files with 26 additions and 29 deletions
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@@ -13,12 +13,12 @@ Kalman filters is therefore problematic \cite{sarkka2013bayesian, Nurminen2014}.
All this shows, that sensor models differ in many ways and are a subject in itself.
A good discussion on different sensor models can be found in \cite{Yang2015} or \cite{Khaleghi2013}.
However, within this work, we use simple models, configured using a handful of empirically chosen parameters and
However, within this work, we utilize simple models, configured using a handful of empirically chosen parameters and
address their inaccuracies by harnessing prior information like the pedestrian's desired destination. Therefore,
instead of examining different sensors and their contribution to the localisation process, we will focus
on the state transition and how to incorporate environmental and navigational knowledge.
A widely used and easy method for modelling the movement of a pedestrian, is the prediction of a new position
A widely used and straightforward method for modelling the movement of a pedestrian, is the prediction of a new position
using both, a walking direction and a to-be-walked distance, starting from the previous position.
If the line-of-sight between the new and the old position intersects a wall, the probability for this
transition is set to zero \cite{Blanchert09-IFF, Koeping14-ILU}.
@@ -45,15 +45,15 @@ Nevertheless, most corridors are still represented by just one topological link.
While the complexity is reduced, it does not allow arbitrary movements and leads to suboptimal trajectories.
Far more flexible and variable geometric spatial models are regularly tessellated approaches e.g. based on grids.
Those techniques are trivially implemented, but yet very powerful.
In \cite{Afyouni2012}, a square-shaped or hexagonal grid covers the entire map.
In \cite{Afyouni2012} a square-shaped or hexagonal grid covers the entire map.
Especially in the area of simultaneous localisation and mapping (SLAM), so-called occupancy-grid approaches are
very popular \cite{elfes1989using, Thrun2003}.
Occupancy grids assign a high probability to cells within the accessible space.
Likewise, cells occupied by obstacles or walls are less likely.
Additionally, every grid cell is able to hold some context information about the environment (e.g. elevators or stairs)
Every grid cell is able to hold some context information about the environment (e.g. elevators or stairs)
or the behaviour of a pedestrian at this particular position (e.g. jumping or running).
A similar approach, presented in \cite{Li2010}, \cite{Ebner-15}, is also used within this work.
A similar approach, presented in \cite{Li2010}, is also used within this work.
Assuming the floorplan is given beforehand, occupied cells can be removed.
The remaining cells are described by their centre/bounding-box and represent free spaces within the environment.
A graph is defined by using the centres as nodes and connecting direct neighbours with edges.