Design, Implementation and Localization of a Mobile Robot for
Urban Site Modeling


This thesis presents a systematic and practical approach to mobile
robot localization in urban environments.  It reflects work on both
system and algorithmic problems.  The methods and ideas presented are
generally applicable to mobile robots operating in urban environments.

On the system level, I have designed and built a functioning
autonomous mobile robot.  The design extended an existing robotic
vehicle with a carefully chosen sensor suite of a digital compass with
an integrated inclinometer, a global positioning unit, and a camera
mounted on a pan-tilt head.  The robot has been equipped with wireless
networking.

I have also designed and implemented a distributed software
architecture for mobile robot navigation.  It addresses important
issues like computation distribution, autonomy, flexibility and
extensibility.  A motion control component controls the robot pose and
drives the robot to its destination.  A graphical user interface
allows for remote control and monitoring.

On the algorithmic level, I have developed a localization system that
employs two methods.  The first method uses odometry, the compass
module and the global positioning sensor.  To reduce the effect of
systematic odometry errors, I have extended an existing calibration
procedure.  An extended Kalman filter integrates the sensor data and
keeps track of the uncertainty associated with it.  When global
positioning data is reliable, the method is used as the only
localization method.  When the data deteriorates, the method detects
it and and seeks additional data by invoking the second localization
method.

The second method is based on visual pose estimation.  It is heavier
computationally but is only used when it is needed.  When invoked, it
stops the robot, chooses a nearby building to use and takes an image
of it.  The pose estimation is done by matching linear features in the
image with a simple and compact model of the building.  A database of
the models is stored on the on-board computer.  No environmental
modifications are required.

I have demonstrated the functionality of the robot and the
localization methods with real-world experiments.