Self-localization and navigation in outdoor environments are fundamental problems a

mobile robot has to solve in order to autonomously execute tasks in a spatial environ-

ment. Techniques based on the Global Positioning System (GPS) or laser-range finders

have been well established but suffer from the drawbacks of limited satellite availability

or high hardware effort and costs. Vision-based methods can provide an interesting al-

ternative, but are still a field of active research due to the challenges of visual perception

such as illumination and weather changes or long-term seasonal effects.

This thesis approaches the problem of robust visual self-localization and navigation using

a biologically motivated model based on unsupervised Slow Feature Analysis (SFA). It

is inspired by the discovery of neurons in a rat’s brain that form a neural representation

of the animal’s spatial attributes. A similar hierarchical SFA network has been shown

to learn representations of either the position or the orientation directly from the visual

input of a virtual rat depending on the movement statistics during training.

An extension to the hierarchical SFA network is introduced that allows to learn an

orientation invariant representation of the position by manipulating the perceived im-

age statistics exploiting the properties of panoramic vision. The model is applied on

a mobile robot in real world open field experiments obtaining localization accuracies

comparable to state-of-the-art approaches. The self-localization performance can be fur-

ther improved by incorporating wheel odometry into the purely vision based approach.

To achieve this, a method for the unsupervised learning of a mapping from slow fea-

ture to metric space is developed. Robustness w.r.t. short- and long-term appearance

changes is tackled by re-structuring the temporal order of the training image sequence

based on the identification of crossings in the training trajectory. Re-inserting images of

the same place in different conditions into the training sequence increases the temporal

variation of environmental effects and thereby improves invariance due to the slowness

objective of SFA. Finally, a straightforward method for navigation in slow feature space

is presented. Navigation can be performed efficiently by following the SFA-gradient,

approximated from distance measurements between the slow feature values at the target

and the current location. It is shown that the properties of the learned representations

enable complex navigation behaviors without explicit trajectory planning.