| dc.description.abstract | Mobile robots have a promising application prospect as they can assist or replace
humans to perform laborious, repetitive or dangerous tasks in various scenarios. There
has been a large number of studies for mobile robot navigation since 1980s, while
terrain traversability estimation is an important topic in this field — estimating if
an area is traversable and how long will it take to drive through is necessary for
navigating the robot and planning paths.
However, most existing terrain traversability estimation methods are designed
based on simple fixed rules and manually tuned parameters, suffering low accuracy
and poor generalization due to their simplicity of structure and biases to the environ-
ment where they are tuned. To address this problem, we proposed a set of data-driven
traversability estimation methods based on Convolutional Neural Networks (CNN),
which are trained and tested them in different simulation environments.
There are 3 main goals for our methods:
1. High accuracy. Accuracy of the result is the core of an traversability estimation
method.
2. Low computational cost. Since most mobile robots are equiped with very lim-
ited computing power and energy, a practical traversability estimation method
should be able to work with a low computational cost.
3. Good generalization. A good traversability estimation method should generalize
to different type of environments or provide a function to automatically fit to a
new environment instead of manual tuning.
In this thesis, we first reviewed some representative conventional terrain
traversability estimation methods and introduced several related fields including mo-
bile robot path planning, localization and map building. Then we proposed our
CNN-based methods, demonstrated how to build the simulation framework and col-
lect terrain samples with driving data. Finally we compared the performance of our
work with benchmark methods in both classification and regression traversability es-
timation tasks on the collected datasets and proved the improvements made by our
methods. | en_US |
