Ali-UI: Enhancing Complex Vision-Language Navigation with Alignment of Unified Map and Instruction Parsing

Visual language navigation (VLN) poses challenges in guiding agents through unseen environments based on natural language instructions. Existing methods either rely on imitation learning, for which training across various complex scenarios remains challenging, or leverage large visual language models (LVLMs) for zero-shot object recognition and expert iterative reasoning for improved scene understanding. Although LVLMs enhance target detection generalization, current VLN methods lack robustness in terms of environmental generalization and struggle with multi-step, coarsely directed instructions. Addressing these challenges, we introduce Ali-UI, a novel vision-language navigation approach that enables agents to navigate from random starting points in unvisited scenes and handle complex multi-step instructions. Specifically, we incorporate continuously accumulating global grid maps and local semantic maps as scene memory by employing frontier-based exploration. Multi-step coarsely directed commands are broken down with the assistance of LLaVA and matched with the scene, considering temporal and spatial alignment. Panoramic data are saved in topological form and queried by instruction segments for sequential navigation. Extensive experiments carried out in simulated environments demonstrate that Ali-UI outperforms existing state-of-the-art methods in terms of flexible human instructions and scene generalization, with the success rate improved by 23.37% and the SPL increased by 19.27% in R2R dataset.