Unordered Landmark Visual Navigation

Image-goal navigation is a fundamental capability for em-bodied AI, yet its practical deployment is strained by strong prior as-sumptions. Existing methods predominantly rely on temporally orderedvideo streams or auxiliary sensors (e.g., depth, LiDAR) to maintain spa-tial consistency. These sequential and multimodal dependencies severelyrestrict scalability, especially when deploying robots using crowd-sourcedor pre-recorded unordered image collections. When temporal priors areremoved, current methods struggle with severe perceptual aliasing, noisyassociations, and catastrophic mapping failures. To address this under-explored challenge, we propose Unordered Landmark Visual Navigation(ULVN), a unified RGB-only framework free from temporal and odo-metric priors. ULVN systematically mitigates error accumulation by in-tegrating mapping, localization, and planning. Specifically, it constructsa robust 2D topological map directly from unstructured images via cali-brated geometric verification and maximum spanning forest refinement.For closed-loop execution, ULVN abandons sequential heuristics, utiliz-ing a graph-based belief propagation filter with entropy-adaptive fusionfor global localization and dynamic subgoal planning. Extensive experi-ments in simulation and real-world deployments demonstrate that ULVNsignificantly outperforms state-of-the-art methods.