  Low cost robots, such as vacuum cleaners or lawn mowers, employ simplistic
and often random navigation policies. Although a large number of sophisticated
localization and planning approaches exist, they require additional sensors
like LIDAR sensors, cameras or time of flight sensors. In this work, we propose
a global localization method biologically inspired by simple insects, such as
the ant Cataglyphis that is able to return from distant locations to its nest
in the desert without any or with limited perceptual cues. Like in Cataglyphis,
the underlying idea of our localization approach is to first compute a pose
estimate from pro-prioceptual sensors only, using land navigation, and
thereafter refine the estimate through a systematic search in a particle filter
that integrates the rare visual feedback. In simulation experiments in multiple
environments, we demonstrated that this bioinspired principle can be used to
compute accurate pose estimates from binary visual cues only. Such intelligent
localization strategies can improve the performance of any robot with limited
sensing capabilities such as household robots or toys.
