The effective application of mobile robotics requires that robots be able to perform tasks with an
extended degree of autonomy. Simultaneous localisation and mapping (SLAM) aids automation by
providing a robot with the means of exploring an unknown environment while being able to position
itself within this environment. Vision-based SLAM benefits from the large amounts of data produced
by cameras but requires intensive processing of these data to obtain useful information. In this dissertation
it is proposed that, as the saliency content of an image distils a large amount of the information
present, it can be used to benefit vision-based SLAM implementations.
The proposal is investigated by developing a new landmark for use in SLAM. Image keypoints are
grouped together according to the saliency content of an image to form the new landmark. A SLAM
system utilising this new landmark is implemented in order to demonstrate the viability of using the
landmark. The landmark extraction, data filtering and data association routines necessary to make
use of the landmark are discussed in detail. A Microsoft Kinect is used to obtain video images as
well as 3D information of a viewed scene. The system is evaluated using computer simulations and
real-world datasets from indoor structured environments. The datasets used are both newly generated
and freely available benchmarking ones.
Dissertation (MEng)--University of Pretoria, 2014.