Master Thesis on “Slow Features for Representation Learning and Brain-Inspired Robot Navigation”

The goal of this thesis to bridge the gap between slow representation learning and brain-inspired navigation. After reviewing the relevant literature from both domains, an experiment will investigate if slow representations can lead to the self organization of structures similar to complex cells in the brain. If the results confirm the hypothesis, another experiment will investigate if the emerging structures can be used for navigation similar to the hand-crafted place-cell network in ViTa-SLAM.

Bio-inspired SLAM

Simultaneous localization and Mapping a.k.a. SLAM has been a well researched topic in the robotics community for over two decades. However, with the recent advances in the Neuroscience yielding deeper insights into the neural correlates of the mammalian brain, there is renewed interest in further exploration of nature inspired SLAM approaches. Rats, bats, and other […]