The project members were divided into three subsystems,
one for each of the project goals.
One of the major advantages with a simulation environment is
the ability to develop algorithms more efficiently, since it
simplifies the trial and error methodology as well as the
iteration process. A robust simulation environment makes future
development easier for several reasons. Firstly, with a
simulation environment it is possible to have total control of
the setup and the noises involved. It enables researchers to
simulate scenarios that would be difficult, or costly, to
implement. Secondly, a simulation environment makes the researchers
practically independent of the hardware during software development.
This also unlocks the ability to estimate the impact of additional
sensors before buying them.
Two different techniques for distance estimation have been
investigated. The first is based on the eye tracking provided
by the glasses. Depending on the distance to the object looked
upon, the relative angle of the gaze vectors change. This is
called vergence and can be used to estimate the distance to
targets at close range.
The second technique is based on identifying objects of roughly
known size, such as faces, in the video stream and based on their
apparent size in the camera calculate the distance to them.
Simultaneous Localization And Mapping (SLAM)
The purpose of SLAM is to give a better understanding of the
environment that the user is located in. SLAM is an algorithm
which estimates landmarks in an environment and its own position
and orientation according to those landmarks. A graphical
interface is used to display the user's position and the
speakers' positions relative to the user. This situational
awareness feature could be a cornerstone in creating sensor
fusion controlled HAs.