Page 145 - Autonomous Mobile Robots

128 Autonomous Mobile Robots

at each epoch the components of the estimated state vector will be correlated.
In addition, due to the fusion of measurements across epochs, even if the meas-
urement noise v is white, the state estimation error will be a colored noise
process. Second, the three preceding sections discussed estimation algorithms
in the order of increasing complexity. The required number of computations to
implement the EKF increases as the size of the state vector increases. Third,
performance will suffer if the application conditions do not match the algorithm
assumptions. If, for example, a P or PV algorithm or too small a value of Q is
used in a “high-dynamic environment,” then the estimated position may have
significant lag relative to the actual position. Fourth, use of Doppler meas-
urements can increase convergence rates, but opens up other modeling issues
[40]. Fifth, if GPS measurements are unavailable for some period of time, the
dynamic model is available to propagate the state estimates; however, accelera-
tion of the system during this time period can have serious adverse effects on the
accuracy of such predictions. This issue can be addressed well by, for example,
properly incorporating an inertial measurement system. Sixth, a recurrent issue
in the approaches of this section is that the stochastic model parameter Q could
not be properly selected. Instead it was used as performance tuning parameter.
Proper incorporation of IMU data into the approach will also allow proper
selection and interpretation of the parameter in a stochastic sense. Addition
of an IMU will increase the cost of the system, but offers the potential for
higher performance (e.g., bandwidth, accuracy, coast time, and sample rate)
and availability.

3.4 INERTIAL NAVIGATION SYSTEM

A strapdown INS incorporates an IMU that measures the acceleration and angu-
lar rate of the system and analytic routines on a computer that integrate the
inertial measurements to provide estimates of the vehicle position, velocity,
and attitude in a desired coordinate frame. This section reviews the strapdown
INS mechanization equations and the dynamic error model of the INS system.
Various methods for fusing the GPS and INS information are reviewed and
discussed in Section 3.5. The example in a 2D world is continued to highlight
various important issues related to GPS–INS integration.
Thisparagraphbrieflydefinesthevariouscoordinateframesthatwillbeused
in the subsequent discussion. All coordinate frames are defined by orthogonal
axes in a right-handed sense. The body frame is attached to and moves with the
vehicle. The inertial measurements are resolved along the axes of the platform
frame. To simplify the discussion, we assume that the body and platform frames
are identical. The navigation frame is attached to the earth at a convenient point
of reference and determines the desired frame in which to resolve the vehicle
position and velocity vectors. The ECEF frame is attached to the center of and

© 2006 by Taylor & Francis Group, LLC

FRANKL: “dk6033_c003” — 2006/3/31 — 16:42 — page 128 — #30