2017
Efficient navigation using slow feature gradients
IROS 2017poster
A model of hierarchical Slow Feature Analysis (SFA) enables a mobile robot to learn a spatial representation of its environment directly from images captured during a random walk. After the unsupervised learning phase a subset of the resulting representations are orientation invariant and code for t…