For mobile robots to navigate autonomously, one of the most important and
challenging task is localisation. Localisation refers to the process whereby a
robot locates itself within a map of a known environment or with respect to
a known starting point within an unknown environment. Localisation of a
robot in unknown environment is done by tracking the trajectory of a robot
whilst knowing the initial pose. Trajectory estimation becomes challenging
if the robot is operating in an unknown environment that has scarcity
of landmarks, is GPS denied, is slippery and dark such as in underground
mines.
This dissertation addresses the problem of estimating a robot's trajectory
in underground mining environments. In the past, this problem has been
addressed by using a 3D laser scanner. 3D laser scanners are expensive and
consume lot of power even though they have high measurements accuracy
and wide eld of view. For this research work, trajectory estimation is accomplished
by the fusion of an ego-motion provided by Time of Flight(ToF)
camera and measurement data provided by a low cost Inertial Measurement
Unit(IMU).
The fusion is performed using Kalman lter algorithm on a mobile robot
moving in a 2D planar surface. The results shows a signi cant improvement
on the trajectory estimation. Trajectory estimation using ToF camera only
is erroneous especially when the robot is rotating. The fused trajectory estimation
algorithm is able to estimate accurate ego-motion even when the
robot is rotating. / [Durban, South Africa] : University of KwaZulu-Natal, 2013.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ukzn/oai:http://researchspace.ukzn.ac.za:10413/11181 |
| Date | 12 September 2014 |
| Creators | Ratshidaho, Thikhathali Terence. |
| Contributors | Tapamo, Jules-Raymond. |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0127 seconds