الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
For a robot to build a map of its surrounding area, it must have accurate
position information within the area, and to obtain accurate position
information within the area, the robot needs to have an accurate map of the
area. This circular problem is what is called Simultaneous Localization and
Mapping (SLAM) problem. There have been several techniques published
for solving this problem.
Truly autonomous mobile robots require the ability to map their
environment in order to explore it. Multi-robot SLAM becomes necessary
once an environment becomes too large. This thesis focuses on the methods
which are used to merge partial maps obtained from multi robots. These
methods are the data association and the inter robot observations. Our work
is mainly concentrated on performing comparison between these two
alignment methods to determine a suitable one of them according to the
environment.
Using these two methods, the robot becomes able to take the decision
which method should be used to estimate transformation matrix, which
convert points in the current robot’s local frame of reference to its global
frame of reference. This allows the robot to merge maps with incurring less
error.
Many simulation experiments are used to demonstrate that the proposed
approach allows multiple robots to map large environments with a better
performance and clarify that robot can easily plan its path by using this
map.