[
    {
        "id": "osp-18081",
        "type": "article-journal",
        "title": "FreeLoc: Online Floorplan Localization via Diffusion-Aided Pose Refinement",
        "author": [
            {
                "family": "Peng",
                "given": "Haocheng"
            },
            {
                "family": "Zhou",
                "given": "Boyang"
            },
            {
                "family": "Hu",
                "given": "Jiarui"
            },
            {
                "family": "Guo",
                "given": "Xiyue"
            },
            {
                "family": "Zhang",
                "given": "Ziyang"
            },
            {
                "family": "Zhao",
                "given": "Boming"
            },
            {
                "family": "Gao",
                "given": "Yifan"
            },
            {
                "family": "Li",
                "given": "Xiao"
            },
            {
                "family": "Bao",
                "given": "Hujun"
            },
            {
                "family": "Cui",
                "given": "Zhaopeng"
            }
        ],
        "URL": "https://omanscience.com/en/articles/freeloc-online-floorplan-localization-via-diffusion-aided-pose-refinement",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Floorplans provide compact and widely available geometric maps for indoor localization, but existing high-performing floorplan-based methods still convert them into dense scene-specific offline databases, tying accuracy, storage, and runtime to the sampling resolution of the discretized pose space. We present FreeLoc, an online RGB-based floorplan localization framework that treats the floorplan as a directly queryable geometric map. FreeLoc introduces an efficient online geometric querying and diffusion-aided refinement scheme, which retrieves plausible pose anchors through on-the-fly floorplan ray querying and refines them into accurate continuous pose estimates. For sequential localization, FreeLoc develops an online likelihood construction strategy that bridges single-frame localization and probabilistic temporal fusion by constructing likelihoods from coarse-sampled candidates and refined pose hypotheses, enabling histogram-filter-based temporal fusion without offline databases. Experiments demonstrate real-time online inference and state-of-the-art performance in both single-frame and sequential localization, while real-world results validate practical deployability in indoor robotic localization scenarios."
    }
]