Rough terrain robotics is a fast evolving field of research and
a lot of effort is deployed towards enabling a greater level of
autonomy for outdoor vehicles. This book demonstrates how the
accuracy of 3D position tracking can be improved by considering
rover locomotion in rough terrain as a holistic problem. Although
the selection of appropriate sensors is crucial to accurately track
the rover's position, it is not the only aspect to consider.
Indeed, the use of an unadapted locomotion concept severely affects
the signal to noise ratio of the sensors, which leads to poor
motion estimates. In this work, a mechanical structure allowing
smooth motion across obstacles with limited wheel slip is used. In
particular, it enables the use of odometry and inertial sensors to
improve the position estimation in rough terrain. A method for
computing 3D motion increments based on the wheel encoders and
chassis state sensors is developed. Because it accounts for the
kinematics of the rover, this method provides better results than
the standard approach. To further improve the accuracy of the
position tracking and the rover's climbing performance, a
controller minimizing wheel slip is developed. The algorithm runs
online and can be adapted to any kind of passive wheeled rover.
Finally, sensor fusion using 3D-Odometry, inertial sensors and
visual motion estimation based on stereovision is presented. The
experimental results demonstrate how each sensor contributes to
increase the accuracy and robustness of the 3D position
estimation.
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