  Vision-and-language navigation (VLN) enables the agent to navigate to a
remote location in 3D environments following the natural language instruction.
In this field, the agent is usually trained and evaluated in the navigation
simulators, lacking effective approaches for sim-to-real transfer. The VLN
agents with only a monocular camera exhibit extremely limited performance,
while the mainstream VLN models trained with panoramic observation, perform
better but are difficult to deploy on most monocular robots. For this case, we
propose a sim-to-real transfer approach to endow the monocular robots with
panoramic traversability perception and panoramic semantic understanding, thus
smoothly transferring the high-performance panoramic VLN models to the common
monocular robots. In this work, the semantic traversable map is proposed to
predict agent-centric navigable waypoints, and the novel view representations
of these navigable waypoints are predicted through the 3D feature fields. These
methods broaden the limited field of view of the monocular robots and
significantly improve navigation performance in the real world. Our VLN system
outperforms previous SOTA monocular VLN methods in R2R-CE and RxR-CE benchmarks
within the simulation environments and is also validated in real-world
environments, providing a practical and high-performance solution for
real-world VLN.
