The main focus of this thesis is finding and validating an efficient visual inertial
navigation system (VINS) algorithm for applications in micro aerial vehicles (MAV).
A typical VINS for a MAV consists of a low-cost micro electro mechanical system
(MEMS) inertial measurement unit (IMU) and a monocular camera, which provides a
minimum payload sensor setup. This setup is highly desirable for navigation of MAVs
because highly resource constrains in the platform. However, bias and noise of lowcost
IMUs demand sufficiently accurate VINS algorithms. Accurate VINS algorithms
has been developed over the past decade but they demand higher computational
resources. Therefore, resource limited MAVs demand computationally efficient VINS
algorithms.
This thesis considers the following computational cost elements in the VINS algorithm:
feature tracking front-end, state marginalization technique and the complexity
of the algorithm formulation. In this thesis three state-of-the-art feature
tracking front ends were compared in terms of accuracy. (VINS-Mono front-end,
MSCKF-Mono feature tracker and Matlab based feature tracker). Four state-ofthe-
art state marginalization techniques (MSCKF-Generic marginalization, MSCKFMono
marginalization, MSCKF-Two way marginalization and Two keyframe based
epipolar constraint marginalization) were compared in terms of accuracy and efficiency.
The complexity of the VINS algorithm formulation has also been compared
using the filter execution time.
The research study then presents the comparative analysis of the algorithms using a
publicly available MAV benchmark datasets. Based on the results, an efficient VINS
algorithm is proposed which is suitable for MAVs
