Wisin gcted GPS Pedesiriar Misvsgatie Sesuicrs
tl Cosa (rer
out
Visier atat GPS Nascgaton
TOO T2008
SEE naan =
TRE Hs 7305 208 TESI 5
East Cocndinahe (rà xc
Figure 7: Field test using phone in two modes while user
walking around a tennis court: the reference solution (green),
GPS position (red), Vision aided GPS navigation (blue)
6. CONCLUTION
This paper concentrates on detecting the most important context
information in personal navigation for users carrying
smartphones. The field test shows that texting mode (which is
the proper mode for vision sensor) can be detected from
accelerometer sensor with the accuracy of 82%. In this mode,
the orientation of the device (i.e. landscape or portrait mode)
can be detected with an accuracy of 93%. Once context
detection is performed, proper computer vision algorithm can be
applied accordingly to find the motion vectors from successive
frames to extract user’s motion.
Morcover, a vision-aided pedestrian navigation algorithm is
proposed to improve GPS solution. To model the characteristics
of the two-dimensional motion of a walking user, Dead
Reckoning algorithm is used as a dynamic model in Kalman
Filter. The measurements fed to the filter are the GPS positions,
velocity and vision-based velocity and the changes in heading
angles when available. Pedestrian field tests were performed to
verify the algorithms. The results are promising for combined
modes and showed great potential for accurate, reliable and
seamless navigation and positioning.
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