  Localization in the environment is an essential navigational capability for
animals and mobile robots. In the indoor environment, the global localization
problem remains challenging to be perfectly solved with probabilistic methods.
However, animals are able to instinctively localize themselves with much less
effort. Therefore, it is intriguing and promising to seek biological
inspiration from animals. In this paper, we present a biologically-inspired
global localization system using a LiDAR sensor that utilizes a hippocampal
model and a landmark-based re-localization approach. The experiment results
show that the proposed method is competitive to Monte Carlo Localization, and
the results demonstrate the high accuracy, applicability, and reliability of
the proposed biologically-inspired localization system in different
localization scenarios.
