  Visual navigation requires a whole range of capabilities. A crucial one of
these is the ability of an agent to determine its own location and heading in
an environment. Prior works commonly assume this information as given, or use
methods which lack a suitable inductive bias and accumulate error over time. In
this work, we show how the method of slow feature analysis (SFA), inspired by
neuroscience research, overcomes both limitations by generating interpretable
representations of visual data that encode location and heading of an agent. We
employ SFA in a modern reinforcement learning context, analyse and compare
representations and illustrate where hierarchical SFA can outperform other
feature extractors on navigation tasks.
