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<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'>I made similar plots to Dr. Anderson’s. A thing to
note is that the errors are higher correlated. This means that averaging a
small number of readings does not help. I have reached the following
conclusions about using consumer grade GPS’s. They will not keep you on
a sidewalk or road. They will not take you to a RoboMagellan cone, but will
get you close provided you have a clear view of the sky. At most locations,
only one of the two WAAS satellites will be in view and it may often be
blocked. I cannot see it from my house. If I were using it for contests 1, 2
and 3 (travel on the order of 100 feet), I would calculate distance and heading
from the contest data. When the robot runs the contest, I would read the
current reported location as its starting position, calculate end coordinates
from distance and heading and use those two locations to navigate. This is not
intended to say that GPSs are useless. Odometry error grow without bound. GPS
errors have a fixed bound. Poor odometry over “short” distances or
good odometry over “long” distances can use a GPS to cap the
error. <o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'>The discussions on filtering versus classification and use
of a Kalman filter are interesting with respect to GPS errors. As mentioned
above, GPS errors are correlated. But they often have large jumps when the
satellites in use change. How should a filter handle the large jumps in
position?<o:p></o:p></span></font></p>
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