  Natural language-based robotic navigation remains a challenging problem due
to the human knowledge of navigation constraints, and destination is not
directly compatible with the robot knowledge base. In this paper, we aim to
translate natural destination commands into high-level robot navigation plans
given a map of interest. We identify grammatically associated segments of
destination description and recursively apply each of them to update a belief
distribution of an area over the given map.We train a destination grounding
model using a dataset of single-step belief update for precise, proximity, and
directional modifier types. We demonstrate our method on real-world navigation
task in an office consisting of 80 areas. Offline experimental results show
that our method can directly extract goal destination from unheard, long, and
composite text commands asked by humans. This enables users to specify their
destination goals for the robot in general and natural form. Hardware
experiment results also show that the designed model brings much convenience
for specifying a navigation goal to a service robot.
