{
  "paper_id": "129e5dbac921c493ffa36a6db89e6948",
  "paper_title": "Indoor Positioning Using Smartphone Sensors: A LIDAR and IMU-Based Approach",
  "paper_authors": [
    "Son Nguyen Trong",
    "Yanlei Gu",
    "Igor Goncharenko"
  ],
  "publication_date": "",
  "publication_venue": "2025 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)",
  "abstract": "The use of indoor positioning technology has become essential for various applications such as navigation, location-based services, and asset tracking. This paper presents a novel approach for indoor positioning utilizing a combination of smartphone built-in sensors and Normal Distributions Transform (NDT) algorithm utilizing lidar sensor data from an iPhone. Our method combines accelerometer, gyroscope, and magnetometer data from the smartphone to estimate the user's initial position, which is then dynamically refined using the Normal Distribution Transform (NDT) algorithm, which aligns LiDAR-derived point clouds with pre-existing 2D maps, thereby enhancing the accuracy of the position estimates. We evaluated the performance of our approach in a real-world indoor environment. Our results demonstrate that our proposed method significantly improves positioning accuracy compared to traditional methods. This study illustrates the potential of utilizing LidDAR sensor data from smartphones for indoor positioning and the advantages of combining multiple sensor modalities for improved accuracy. Our approach contributes to the development of practical and low-cost indoor positioning systems using only smartphones.\n",
  "keywords": [
    "indoor positioning",
    "LIDAR",
    "smartphone sensors",
    "NDT matching",
    "2D map"
  ],
  "paper_institutions": [
    "Ritsumeikan University",
    "Hiroshima University"
  ],
  "output_language": "zh",
  "provider": "doubao",
  "model": "deepseek-v4-flash",
  "prompt_template": "simple",
  "extracted_at": "2026-08-18T10:30:53.127601Z",
  "template_tag": "simple",
  "summary": "### 摘要\n\n本文针对室内环境中GNSS信号不可靠、传统PDR因IMU漂移而累积误差的问题，提出一种仅依赖智能手机内置传感器与LiDAR的室内定位方法。相关研究包括UWB、Wi-Fi、蓝牙等基础设施依赖方法以及PDR等基础设施无关方法，但前者需要预安装、后者存在漂移。方法首先利用加速度计、陀螺仪和磁力计进行PDR初始定位，再将iPhone LiDAR深度图像转换为三维点云，经RANSAC去除地面和天花板并投影为鸟瞰图，最后用NDT算法将LiDAR点云与AutoCAD生成的2D矢量地图匹配，通过最小化负对数似然（-ln L(R,T) = -Σ ln(p(Rx_i+T))）求最优刚体变换，并用扩展卡尔曼滤波融合PDR与NDT结果。实验在立命馆大学Creation Core二楼约130米走廊进行，使用iPhone 14 Pro，测试路径包含多个转弯并回到起点；该环境存在典型磁场干扰，用于验证真实条件下的有效性。结果表明PDR-only的RMSE为16.37米，集成NDT后降至1.71米，显著抑制漂移。未来计划探索更多传感器组合、不同室内环境测试、自适应校准以减少传感器偏差，并提升实时处理能力。该方法适用于室内导航、应急响应、增强现实和资产追踪等场景，且仅需智能手机，成本低、实用性强。核心机制是NDT将地图点云划分为1米网格并建立高斯分布，通过配准修正PDR位置。\n\n### 关键要点\n\n- 论文提出结合智能手机IMU与LiDAR的室内定位方法，利用NDT匹配修正PDR累积漂移。\n- 系统流程分为三个阶段：PDR定位、LiDAR点云投影、NDT地图匹配，最终通过扩展卡尔曼滤波融合输出。\n- PDR使用加速度计、陀螺仪和磁力计数据，经过滤波校准后检测步态、估计步长和航向，并按位置更新公式递推。\n- LiDAR深度图像先经内参矩阵转为相机坐标系三维点，再经外参矩阵转到世界坐标系，形成点云。\n- 使用RANSAC算法移除点云中的地面和天花板平面，再转换为鸟瞰图表示，便于与2D地图配准。\n- 2D矢量地图由AutoCAD根据图像地图生成，并施加比例因子保证真实距离；墙线被均匀采样为最大间隔10 cm的点云。\n- NDT将参考点云所在空间划分为1 m×1 m网格，每个包含足够点的网格用均值和协方差建立二维高斯分布。\n- NDT匹配通过最小化负对数似然函数求解最优旋转和平移，将LiDAR点云对齐到地图坐标系。\n- 实验在立命馆大学Creation Core二楼约130米走廊进行，使用iPhone 14 Pro，路径包含四个转弯并回到起点。\n- 与仅用PDR相比，集成NDT后轨迹更接近真实路径，RMSE从16.37米降至1.71米。\n- 该方法仅依赖智能手机，无需外部基础设施，适合室内导航、应急响应、增强现实和资产追踪等场景。\n- 未来工作包括测试更多传感器组合、不同室内环境、自适应校准以减少传感器偏差，并提升实时处理能力。"
}