{
  "paper_id": "242b4cb7ba8ecd55ced0722712d716eb",
  "paper_title": "Gain Without Pain: Enabling Fingerprinting-based Indoor Localization using Tracking Scanners",
  "paper_authors": [
    "Hamada Rizk",
    "Hirozumi Yamaguchi",
    "Moustafa Youssef",
    "Teruo Higashino"
  ],
  "publication_date": "2020-11-03",
  "publication_venue": "SIGSPATIAL '20",
  "abstract": "Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Towards achieving this goal, WiFi fingerprinting-based indoor localization systems have been proposed. However, fingerprinting involves significant effort; especially when done at high density; and needs to be repeated with any change in the deployment area. While a number of recent systems have been introduced to reduce the calibration effort, these still trade overhead with accuracy. In this paper, we present LiPhi: an accurate system for enabling fingerprinting-based indoor localization systems without the associated data collection overhead. This is achieved by leveraging the sensing capability of transportable laser range scanners (LRSs) to automatically label WiFi signal scans, which can subsequently be used to build (and maintain) localization models. As part of its design, LiPhi has modules to associate WiFi scans with the unlabeled traces obtained from as few as one LRS as well as provisions to train a robust deep learning model. Evaluation of LiPhi using Android phones in two realistic testbeds shows that it can match the performance of manual fingerprinting techniques under the same deployment conditions without the overhead associated with the traditional fingerprinting process. In addition, LiPhi improves upon the median localization accuracy obtained from crowdsourcing-based and fingerprinting-based systems by 181% and 297% respectively, when tested with data collected a few months later.",
  "keywords": [
    "Indoor localization",
    "WiFi fingerprinting",
    "Laser range scanner",
    "Deep learning",
    "Crowdsourcing"
  ],
  "paper_institutions": [
    "Osaka University",
    "E-JUST",
    "Tanta University",
    "Alexandria University"
  ],
  "output_language": "zh",
  "provider": "doubao",
  "model": "ark-code-latest",
  "prompt_template": "simple",
  "extracted_at": "2026-08-16T19:47:22.497489Z",
  "template_tag": "simple",
  "summary": "### 摘要\nLiPhi系统旨在解决传统WiFi指纹定位中数据采集开销大、对环境变化敏感的问题。它利用可移动的激光测距扫描仪（LRS）自动为众包采集的WiFi信号标注位置，从而无需人工干预即可构建和更新指纹数据库。系统包含四个核心模块：LRS轨迹估计、WiFi轨迹估计（基于空间规则）、轨迹匹配（最小化LRS与WiFi估计轨迹的逐点距离）以及深度神经网络定位模型构建。评估在两个真实测试环境（办公室240m²和整层629m²）中进行，使用多款Android手机采集数据。结果表明，LiPhi在新鲜指纹情况下与传统手动指纹方法精度相当，而在数月后的数据测试中，中位定位精度比基于众包和传统指纹的系统分别提高181%和297%。系统还通过数据增强（随机丢弃AP和加噪）和批归一化增强了模型鲁棒性。其核心机制是基于WiFi信号相对强度和LRS精确轨迹的匹配，实现了零额外开销的自动标注与模型更新。LiPhi适用于大型商场、办公区等需要动态更新指纹的场景，且仅需临时部署少量LRS即可工作。\n\n### 关键要点\n- LiPhi通过可移动的激光测距扫描仪（LRS）自动标注WiFi扫描，免除了传统指纹采集的人工劳动。\n- 系统仅需一个LRS即可通过分段覆盖区域的方式构建完整指纹数据库。\n- WiFi轨迹估计模块利用参考AP的欧氏距离关系生成空间规则，并根据规则匹配得分选出候选位置。\n- 为抵抗信号瞬时波动，LiPhi采用滑动窗口（默认5个扫描）进行异常剔除和加权质心平滑。\n- 轨迹匹配通过最小化窗口内LRS轨迹与WiFi估计轨迹的累计欧氏距离，实现精确标签分配。\n- 深度定位模型采用全连接网络，输出为2D坐标，以MSE为损失函数，激活函数为tanh和线性。\n- 数据增强方法包括随机丢弃部分AP（模拟AP变化）和添加高斯噪声（模拟设备差异），提升泛化能力。\n- 在办公室测试床，LiPhi中位误差约0.79米；在整层测试床，LRS标注使中位误差比仅用WiFi粗标提高351.8%。\n- 与WiDeep和HybridLoc对比，新鲜指纹时精度相当，但四个月后LiPhi优于二者分别达297%和181%。\n- LiPhi支持通过迁移学习快速更新模型，只需临时部署LRS即可应对环境变化，无需重新大规模采集。\n- 系统所需LRS数量少（测试中办公室4个、整层6个），且可移动部署，成本低。",
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}