{
  "paper_id": "7cc37d1ec59938d1cf3767ef0d930535",
  "paper_title": "A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement",
  "paper_authors": [
    "Kenji Koide",
    "Jun Miura",
    "Emanuele Menegatti"
  ],
  "publication_date": "March-April 2019",
  "publication_venue": "International Journal of Advanced Robotic Systems",
  "abstract": "It is important to measure and analyze people behavior to design systems which interact with people. This article describes a portable people behavior measurement system using a three-dimensional LIDAR. In this system, an observer carries the system equipped with a three-dimensional Light Detection and Ranging (LIDAR) and follows persons to be measured while keeping them in the sensor view. The system estimates the sensor pose in a three-dimensional environmental map and tracks the target persons. It enables long-term and wide-area people behavior measurements which are hard for existing people tracking systems. As a field test, we recorded the behavior of professional caregivers attending elderly persons with dementia in a hospital. The preliminary analysis of the behavior reveals how the caregivers decide the attending position while checking the surrounding people and environment. Based on the analysis result, empirical rules to design the behavior of attendant robots are proposed.",
  "keywords": [
    "3-D LIDAR",
    "people detection and tracking",
    "behavior analysis"
  ],
  "paper_institutions": [
    "Toyohashi University of Technology",
    "University of Padova"
  ],
  "output_language": "zh",
  "provider": "doubao",
  "model": "ark-code-latest",
  "prompt_template": "simple",
  "extracted_at": "2026-08-16T19:46:48.473709Z",
  "template_tag": "simple",
  "summary": "### 摘要\n本文针对现有静态传感器和可穿戴传感器系统难以实现长期、广域且与环境和他人位置相关联的人群行为测量的问题，提出了一种基于三维激光雷达（3-D LIDAR）的可穿戴便携式人群行为测量系统。该系统由观察者背负3-D LIDAR（Velodyne HDL-32e）和计算机，跟随目标人员并保持其在传感器视野内，通过离线构建环境地图和在线定位与人员跟踪实现行为数据采集。该系统采用基于图优化的SLAM方法，并结合地面平面约束（室内）和GPS位置约束（室外）来补偿累积旋转误差；在线阶段使用无迹卡尔曼滤波（UKF）融合角速度与NDT扫描匹配进行传感器定位，并利用分裂合并聚类和基于切片特征的人体分类器进行人员检测和跟踪。为验证系统性能，作者在室内外环境进行了SLAM失效评估、传感器定位实验、人员检测实验，并与基于静态RGB-D相机网络的OpenPTrack系统对比，结果表明其跟踪精度与静态系统相当（位置平均差异约0.08–0.1 m），而测量范围可通过移动覆盖更大区域。在医院的实地试验中，记录了33段专业护理人员照护痴呆症老年人的行为（总时长约52分钟），初步分析发现护理人员通常保持在老年人侧方约0.6米的个人距离，在转弯时多走外侧以保持视野，并随坡度调整前后位置但保持侧向距离恒定。基于这些分析，文章提出了设计陪护机器人的经验规则。系统软件和记录数据已开源发布。"
}