Machine Learning-based Indoor Localization for Micro Aerial Vehicles
Keywords
Loading...
Authors
Issue Date
2016-08-26
Language
en
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Micro Aerial Vehicles (MAVs) are versatile platforms: their applications range from surveillance
to search and rescue operations. However, MAVs have limited processing power
due to their small size and cannot fall back on standard localization techniques in the
indoor environment. To address this issue, an efficient on-board localization technique
using machine learning was developed in the scope of this thesis.
The vision-based approach estimates x, y-coordinates within a known and modifiable
indoor environment. Its computational power is scalable to different platforms, trading off
speed and accuracy. Histograms of textons—small characteristic image patches—are used
as features in a k-Nearest Neighbors (k-NN) algorithm. Several possible x, y-coordinates
that are outputted by this regression technique are forwarded to a particle filter to neatly
aggregate the estimates and solve positional ambiguities. To predict the performance of
the algorithm in different environments an evaluation technique is developed. It compares
actual texton histogram similarities to ideal histogram similarities based on the distance
between the underlying x, y-positions. The technique assigns a loss value to a given set
of images, enabling comparisons between environments and the identification of critical
positions within an environment. To compare maps before modifying an environment, a
software tool was created that generates synthetic images to simulate those taken during
an actual flight.
We conducted flight tests to evaluate the performance of the approach. A comparison of the
localization technique with the ground truth showed promising results: the algorithm has
a localization accuracy of approximately 0.6m on a 5m×5m area at a runtime of 32 ms on
board of an MAV. In a triggered landing experiment, the MAV correctly landed in or close
to specified areas. The map evaluation technique was applied to various high-resolution
images to identify suitable maps.
The presented approach is based on three pillars: (i) a shift of processing power to a preflight
phase to pre-compute computationally complex steps, (ii) lightweight and adaptable
algorithms to ensure real-time performance and portability to different platforms, (iii)
modifiable environments that can be tailored to the presented algorithm. These pillars
build a foundation for efficient localization in various GPS-denied environments.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen