RSRNav: Reasoning Spatial Relationship for Image-Goal Navigation
This work improves navigation for robotics and AI systems by enhancing spatial reasoning, though it is incremental as it builds on existing methods.
The paper tackled the problem of image-goal navigation by addressing challenges in directional accuracy and viewpoint inconsistencies, resulting in superior navigation performance on benchmark datasets, especially in user-matched goal settings.
Recent image-goal navigation (ImageNav) methods learn a perception-action policy by separately capturing semantic features of the goal and egocentric images, then passing them to a policy network. However, challenges remain: (1) Semantic features often fail to provide accurate directional information, leading to superfluous actions, and (2) performance drops significantly when viewpoint inconsistencies arise between training and application. To address these challenges, we propose RSRNav, a simple yet effective method that reasons spatial relationships between the goal and current observations as navigation guidance. Specifically, we model the spatial relationship by constructing correlations between the goal and current observations, which are then passed to the policy network for action prediction. These correlations are progressively refined using fine-grained cross-correlation and direction-aware correlation for more precise navigation. Extensive evaluation of RSRNav on three benchmark datasets demonstrates superior navigation performance, particularly in the "user-matched goal" setting, highlighting its potential for real-world applications.