Improved Inertial/Odometry/GPS Positioning of Wheeled Robots Even in GPS-Denied Environments - Global Navigation Satellite Systems: Signal,Theory and Applications - page 277

with velocity updates during outages, and of approximately 82.0% over KF with RISS without
any updates during outages. Considering the maximum error in horizontal positioning in
the first trajectory, the KF with RISS and velocity updates during GPS outages achieved an
average improvement of approximately 99.0% over KF with full IMU without any updates
during outages, of approximately 33.2% over KF with full IMU with velocity updates during
outages, and of approximately 88.8% over KF with RISS without any updates during outages.
These results show the superiority of the proposed localization solution.

One problem unique to small wheeled robots with strap-down navigation systems is that
there is a great deal of chassis rigidity that passes along any disturbances felt at the wheels
of the robot. Small, low-cost robots do not have suspension systems found on full-size
vehicles which prevent many disturbances from being measured by the accelerometers
of a strap-down IMU. A future investigation is required regarding low-cost measures for
dampening some of the vibrations caused by small obstacles and imperfections on the road
surface. Prospective researchers should make a careful selection of tires for their small mobile
robot that allow moderate deformation to small obstacles while preserving sufficient shape to
maintain reliable estimates for velocities measured by the wheel encoders.

Kalman filtering is a good technique for reducing the stochastic error of a system since
it requires little processing time compared to other algorithms. It is a suitable choice for
deployment in low-cost, low-power, low-form-factor systems such as those found on small
mobile robots. Further study is required to determine the performance of the techniques
outlined in this work in the context of an embedded system operating in real-time.

6. Acknowledgements

To our family and friends for their love, support and commitment. This chapter wouldn't

have been possible without them.

7. References
Borenstein, J., Everett, H., Feng, L. & Wehe, D. (1997). Mobile robot positioning: Sensors and
techniques, Journal of Robotic Systems 14(4): 231-249.
Chong, K. S. & Kleeman, L. (1997). Accurate odometry and error modelling for a mobile robot,
Proceedings of the 1997 IEEE International Conference on Robotics and Automation , Vol. 4,
Albuquerque, NM, pp. 2783-2788.
Cox, I. J. (1991). Blanche - an experiment in guidance and navigation of an autonomous robot
vehicle, IEEE Transactions on Robotics and Automation 7(2): 193-204.

Farrell, J. A. & Barth, M. (1998). The Global Positioning System & Inertial Navigation ,

McGraw-Hill.
Grewal, M. S., Weill, L. R. & Andrews, A. P. (2007). Global Positioning Systems, Inertial
Navigation, and Integration , John Wiley and Sons.
HG1700 Inertial Measurement Unit (2009).
URL: http://www51.honeywell.com/aero/common/documents/myaerospacecatalog-documents
/Missiles-Munitions/HG1700_Inertial_Measurement_Unit.pdf
IMU300CC - 6DOF Inertial Measurement Unit (2009).
URL: www.xbow.com/Products/Product_pdf_files/Inertial_pdf/IMU300CC_Datasheet.pdf