الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Simultaneous Localization And Mapping (SLAM) is a system that comprises the estimation
of both robot pose and map structure when moving in an unknown and unstructured
environment. SLAM system may utilize sensors such as inertial sensors, laser range nders,
GPS, and cameras. Laser range nders had long been used to sense the surrounding
environment. Recently, there had been much interest in visual SLAM using camera only
to reduce system cost. Estimation of robot pose using visual SLAM is not easy due to
the absence of depth information in camera images and the strong nonlinearity of the
perspective projection function. This also imposes computational challenges on visual
SLAM techniques, especially when the robot moves in a large map. As the map size
increases, the computational cost for updating the map and consulting it for localizing a
robot becomes expensive.
In this work, a hybrid visual/inertial SLAM system is developed where the measurements
from a single camera and an Inertial Measurement Unit (IMU) are fused together. The
main objective of the new approach is to reduce the computational load in large environments
which can be described as the system scalability. The new system comprises
two stages: at the rst stage, incremental position changes, orientation, velocity, and 3-
D relative positions of landmarks are estimated from the sensor measurements. A novel
visual-inertial fusion technique for inertial and visual data is devised utilizing the inherent
sparsity in the optimization matrix. While, in the second stage, a map of the environment
is maintained using the results of the rst stage. A novel map representation as a graph
of interconnected landmark points is proposed. The landmark positions are extracted
through an exactly linear optimization process of the map. Since map update is the most
expensive process, this linearity is a signi cant improvement compared to the commonly
v
vi used nonlinear optimization techniques. The computational complexity of the second
stage is of the order O(N2).
The relative motion estimation module is implemented and tested against standard benchmark
data sets, a custom dataset is also used to investigate the performance and accuracy
of the proposed system. The results indicate that the proposed system is stable and accurate
compared to the state of the art methods. Simulations of the whole system are
performed to verify the feasibility and accuracy of it.