Changes

tex_review/chapters/relatedwork.tex

@@ -21,7 +21,7 @@ Most localization approaches differ mainly in how the transition and evaluation
 
 The system's dynamics describe a pedestrian's potential movement within the building.
 This can be formulated as the question \emph{``Given the pedestrian's current position and heading are known, where could he be after a certain amount of time?''}.
-Obviously, the answer to this question depends on the pedestrian's walking behavior, any nearby architecture and thus the building's floorplan.
+Obviously, the answer to this question depends on the pedestrian's walking behavior, any nearby architecture and thus the building's floor plan.
 %
 Assuming the pedestrian to walk almost straight towards his current heading with a known, constant walking speed, the most basic form of state transition simply rejects all movements, where the line-of-sight between current position and potential destination is blocked by an obstacle \cite{Woodman08-PLF, Blanchart09}.
 %
@@ -30,14 +30,14 @@ The intersection-test can be costly, depending on the number of used particles a
 Furthermore, it is limited mainly to 2D transitions within the plane. 
 Smooth 3D transitions, like walking stairs, would require much more complex intersection tests \cite{Afyouni2012}.
 
-To overcome both limitations, the building's floorplan can be used to derive a graph-based structure, like voronoi diagrams or fixed-distance grids, moving all costly intersection tests into a one-time offline phase \cite{Ebner-16, Hilsenbeck2014}.
+To overcome both limitations, the building's floor plan can be used to derive a graph-based structure, like voronoi diagrams or fixed-distance grids, moving all costly intersection tests into a one-time offline phase \cite{Ebner-16, Hilsenbeck2014}.
 Hereafter, graph-based random walks along the created data-structure can be used as a fast transition approximation.
 Smooth transitions in 3D space can be achieved by generating nodes and edges along stairs and elevators.
 Furthermore, the nodes can be used to store additional information, like their distance towards a pedestrian's desired destination.
 Such information can be included during the transitions step, \eg{} increasing the likelihood of all potential movements that approach this destination \cite{Ebner-16}.
 
 However, the graph-based approach also imposes some potential issues. When using a gridded graph, the spacing between adjacent
-nodes directly represents the transition's accuracy. Likewise, the amount of required memory to represent the floorplan 
+nodes directly represents the transition's accuracy. Likewise, the amount of required memory to represent the floor plan 
 scales about quadratically with this spacing. Even though nodes/edges are only created for actually walkable areas (like a sparse cube),
 large buildings require millions of nodes and might not fit into memory at once.
 Furthermore, (large) outdoor regions between adjacent buildings require unnecessarily large amounts
@@ -46,7 +46,7 @@ they usually suffer from reduced accuracy for large open spaces, as many impleme
 
 We therefore present a novel technique based on continuous walks along a navigation mesh.
 Like the graph, the mesh, consisting of triangles sharing adjacent edges,
-is created once during an offline phase, based on the building's 3D floorplan.
+is created once during an offline phase, based on the building's 3D floor plan.
 Using large triangles reduces the memory footprint dramatically (a few megabytes for large buildings)
 while still increasing the quality (triangle-edges directly adhere to architectural-edges) and allows
 for truly continuous transitions along the surface spanned by all triangles. 
@@ -60,12 +60,12 @@ During a one-time offline-phase, a multitude of reference measurements are condu
 During the online-phase the pedestrian's location is then inferred by comparing those prior measurements against live readings.
 Based on this pioneering work, many further improvements where made within this field of research \cite{PropagationModelling, ProbabilisticWlan, meng11}. 
 However, despite a very high accuracy up to \SI{1}{\meter}, classic fingerprinting approaches suffer from tremendous setup- and maintenance times. 
-\add{For this reason, some alternative approaches were presented to speed up the offline phase. 
+\add{For this reason, some alternative approaches were presented to speed up the offline-phase. 
 In \cite{Guimaraes16} the positions of recorded references are interpolated between the start and end of some reference path, based on the pedestrians gait cycle.  
-Unrecorded positions are then interpolated using the flood fill algorithm.  
+Unrecorded positions are then obtained using the flood fill algorithm.  
 However, for old buildings with many nooks and crannies this might cause problems as the RSSI can differ highly within a few meter, especially in the entrance area of thick-walled rooms. 
 This could open the need for more advanced map interpolation methods or a higher number and density of reference paths to walk.
-Another often considered alternative is using robots instead of human workforce \cite{he2016wi, yeh2009indoor}}, still this seems not to be a valid option for old buildings with limited accessibility due to uneven grounds and small stairs.
+Another often considered alternative is using robots instead of human workforce \cite{he2016wi, yeh2009indoor}}, still this seems not to be a valid option for old buildings with limited accessibility for robots due to uneven grounds and small stairs.
  
 %wifi, signal strength 
 Signal strength prediction models are a well-established field of research to determine signal strengths for arbitrary locations by using an estimation model instead of real measurements.