Global Positioning System Reference
In-Depth Information
for these robots are autonomous lawnmowers and motorized wheelchairs. These devices are
low-cost and are used on terrain that is not flat. GPS can be used to provide three-dimensional
knowledge of the mobile robot's position. Unfortunately, GPS suffers from outages when
line-of-sight is blocked between the robot and GPS satellites. These outages are caused
by operating the robot in and around buildings, dense foliage and other obstructions. An
inertial measurement unit (IMU), with three accelerometers and three gyroscopes, is a good
choice in lieu of GPS during outages for providing a 3-D positioning solution. Since a
low-cost solution is needed for certain mobile robots, a low-cost IMU based on a micro
electro-mechanical system (MEMS) has to be used. However, MEMS-based inertial sensors
suffer from several complex errors such as biases; moreover these errors have influential
stochastic parts. Since inertial navigation systems (INS) involve integration operations using
sensor readings, the subsequent errors will accumulate and cause a rapid degrade in the
quality of position estimate. Odometry using wheel encoders is another type of dead
reckoning that provides limited localization information, mostly two-dimensional (2-D). This
information is not subject to the same magnitude of errors as the IMU, provided that the
vehicle does not encounter excessive skidding or slipping. But these 2-D solutions will not be
adequate if the robot often moves outside the horizontal plane.
While 2-D and 3-D solutions using sensors in a full-sized vehicle have been done in the work
to-date, further research is needed in the area of 3-D localization of small wheeled mobile
robots operating in large 3-D terrain. The majority of the previous work using small mobile
robots shows that the terrain is flat and the paths of the robots are small (for example (Ohno
et al., 2003)(Ollero et al., 1999)(Chong & Kleeman, 1997)). This work attempts to bridge the gap
between full-sized vehicle navigation in 3-D and navigation of small wheeled mobile robots
over large paths in uneven terrain. Furthermore, this work will provide a 3-D solution for a
small wheeled mobile robot that is required to travel distances in excess of 1 km over hilly
terrain.
This work aims at combining the advantages of inertial sensors and odometry while
mitigating their disadvantages to provide enhanced low-cost mobile robot 3-D localization
capabilities during GPS outages. This will be achieved through the use of a Kalman Filter
(KF) that integrates odometry from wheel encoders, low cost MEMS-based inertial sensors
and GPS in a loosely-coupled scheme. To enhance the performance and lower the cost further,
the proposed technique uses a reduced inertial sensor system (RISS). To further enhance the
solution during GPS outages, velocity updates computed from wheel speeds are used to
reduce the drift of the estimated solution. Moreover, this work proposes the development
of a predictive error model used in a KF for estimating the errors in positions, velocities and
azimuth angle from RISS mechanization. The experimental results will show that this error
model when combined in a KF with 3-D measurement updates of velocities using forward
speed from encoders together with pitch and azimuth estimates is a good technique for greatly
reducing localization errors.
The structure of the rest of this chapter is as follows: Section 2 presents the methodology used.
It describes the equations used to implement the RISS mechanization and KF error-models.
Section 3 is a description of the mobile robot and the setup used in the experiments. Section
4 presents the results and discussion of this work.
Finally, Section 5 is the conclusion and
discussion of future work.
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