+ All Categories
Home > Documents > MapRepresentationandPathPlanning for$Indoor$Navigation$on ...chet/engr/research... ·...

MapRepresentationandPathPlanning for$Indoor$Navigation$on ...chet/engr/research... ·...

Date post: 25-Aug-2020
Category:
Upload: others
View: 1 times
Download: 0 times
Share this document with a friend
1
Map Representation and Path Planning for Indoor Navigation on a Smartphone Chet Gnegy*, Balajee Kannan Department of Electrical Engineering, University of Pittsburgh, Pi#sburgh, PA 15261* Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15289† The NavPal Applica.on Computa8onally intensive localiza8on, planning, and predic8on processes on the Android Nexus S Smartphone Localiza8on to pinpoint user’s loca8on in a public building Path Planning to help user navigate to desired des8na8on Capability to interpret floor plans that have not been hand prepared A lightweight representa8on of a hierarchical map network that allows for landmarks or obstacles to be tagged by user Des8na8on predic8on based on user’s current path and movement histories. Localiza8on Map Representa8on Path Planner Des8na8on Predic8on WiFi Signal Mapping Onboard Gyroscope User Interface Campus Building Floor Room Ver8ces Edges Grid Hierarchical Map Network Map hierarchy is represented by a connected graph of ver8ces and edges Each level has connec8ons (i.e. roads, elevators, or doorways) that link the ver8ces The user interacts beneath the lowest hierarchy on the grid The grid supports annota8ons, as well as more fine grain path planning User’s movement between rooms is sent to the des8na8on predictor and the most likely des8na8on is returned to the path planner Path Planning Recursively searches through the network of ver8ces and edges un8l a connec8on is found If a common ancestor cannot be found between the user’s room and the des8na8on’s room, no path exists Coarse grain planning is done on ver8ces to determine the closest elevator or staircase en route to the des8na8on Planner narrows in on user posi8on and only uses fine grain grid planning to lead them to that nearest elevator or stairwell Path planning will lead user to specified landmark or predicted des8na8on Interpre.ng the Map Black and white color data on map gives good es8mate of boundaries and free space Standard PDF files can be converted to vector data supplying the shape of the room PDF obtained from Carnegie Mellon website Color data and vector data are combined to give an accurate representa8on of the walls on the floor plan Acknowledgements I would like to thank my colleagues Evan Glasgow and Piotr Yornadov for their important contribu8ons to the NavPal applica8on this summer. Thanks to Google for sponsoring the development of this applica8on. PROVIDED PDF Exit
Transcript
Page 1: MapRepresentationandPathPlanning for$Indoor$Navigation$on ...chet/engr/research... · MapRepresentationandPathPlanning for$Indoor$Navigation$on$a$Smartphone$ Chet$Gnegy*,$BalajeeKannan†

Map  Representation  and  Path  Planning  for  Indoor  Navigation  on  a  Smartphone  

Chet  Gnegy*,  Balajee  Kannan†  Department  of  Electrical  Engineering,  University  of  Pittsburgh,  Pi#sburgh,  PA  15261*  

Robotics  Institute,  Carnegie  Mellon  University,  Pittsburgh,  PA  15289†  

The  NavPal  Applica.on   Computa8onally  intensive  localiza8on,  planning,  and  predic8on  processes  on  the  Android  Nexus  S  Smartphone   Localiza8on  to  pinpoint  user’s  loca8on  in  a  public  building   Path  Planning  to  help  user  navigate  to  desired  des8na8on   Capability  to  interpret  floor  plans  that  have  not  been  hand  prepared       A  lightweight  representa8on  of  a  hierarchical  map  network  that  allows  for  landmarks  or  obstacles  to  be  tagged  by  user   Des8na8on  predic8on  based  on  user’s  current  path  and  movement  histories.    

Localiza8on   Map  Representa8on  

Path  Planner  

Des8na8on  Predic8on  

WiFi  Signal  Mapping  

Onboard  Gyroscope  User  Interface  

Campus  

Building  

Floor  

Room  

Ver8ces  

Edges  

Grid  

Hierarchical  Map  Network   Map  hierarchy  is  represented  by  a  connected  graph  of  ver8ces  and  edges   Each  level  has  connec8ons  (i.e.  roads,  elevators,  or  doorways)  that  link  the  ver8ces   The  user  interacts  beneath  the  lowest  hierarchy  on  the  grid   The  grid  supports  annota8ons,  as  well  as  more  fine  grain  path  planning   User’s  movement  between  rooms  is  sent  to  the  des8na8on  predictor  and  the  most  likely  des8na8on  is  returned  to  the  path  planner      

Path  Planning   Recursively  searches  through  the  network  of  ver8ces  and  edges  un8l  a  connec8on  is  found   If  a  common  ancestor  cannot  be  found  between  the  user’s  room  and  the  des8na8on’s  room,  no  path  exists   Coarse  grain  planning  is  done  on  ver8ces  to  determine  the  closest  elevator  or  staircase  en  route  to  the  des8na8on   Planner  narrows  in  on  user  posi8on  and  only  uses  fine  grain  grid  planning  to  lead  them  to  that  nearest  elevator  or  stairwell   Path  planning  will  lead  user  to  specified  landmark  or  predicted  des8na8on  

Interpre.ng  the  Map   Black  and  white  color  data  on  map  gives  good  es8mate  of  boundaries  and  free  space     Standard  PDF  files  can  be  converted  to  vector  data  supplying  the  shape  of  the  room   PDF  obtained  from  Carnegie  Mellon  website   Color  data  and  vector  data  are  combined  to  give  an  accurate  representa8on  of  the  walls  on  the  floor  plan  

Acknowledgements  I  would  like  to  thank  my  colleagues  Evan  Glasgow  and  Piotr  Yornadov  for  their  important  contribu8ons  to  the  NavPal  applica8on  this  summer.  Thanks  to  Google  for  sponsoring  the  development  of  this  applica8on.    

PROVIDED PDF

Exit  

Recommended