RCPNav: Reversible Cruise–Precision Control with Keyframe Memory and Landmark Graphs for Aerial Vision-and-Language Navigation

Aerial vision-and-language navigation requires a UAV to use onboard visual observations to locate a destination described in natural language, with applications in urban inspection and search. Reaching the destination requires efficient long-distance flight as well as accurate localization near the target. A one-way coarse-to-fine controller may switch prematurely to local search and cannot return to cruise even if the target remains distant. Long flights also make it difficult to retain useful past observations and reason about spatial relations among named landmarks. We propose RCPNav, which combines three components: a reversible cruise–precision controller that can return to cruise when the target is estimated to be farther away; selective keyframe memory that retrieves earlier views conditioned on the instruction; and a language-grounded landmark graph that organizes named places to support goal prediction. On all three evaluation splits of the refined CityNav benchmark, RCPNav achieves the highest success rate and success weighted by path length among the compared methods. Ablation experiments with separately retrained variants show that all three components improve navigation performance.