{
  "paper_id": "5ef59ddbaba769099bdcd643801e62ed",
  "paper_title": "Lidar-based Human Detection for a Mobile Robot in Social Environment using Deep Learning",
  "paper_authors": [
    "Alif Wicaksana Ramadhan",
    "Bima Sena Bayu Dewantara",
    "Setiawardhana",
    "Fursan Akzada"
  ],
  "publication_date": "",
  "publication_venue": "2023 International Electronics Symposium (IES)",
  "abstract": "Nowadays, robotics implementation spans industries such as manufacturing, healthcare, and logistics, integrating AI-driven capabilities to enhance efficiency and collaboration in diverse operational environments. The ability to respond and interact while considering human presence is crucial for a mobile robot to operate effectively and be accepted in a human social environment. It is necessary to make a robot that can detect the presence of humans around it to make it capable of navigating while considering the social environment. Meanwhile, considering that the lidar sensor is almost always available in many robot navigation cases, it becomes an accessible tool for detecting the presence of humans in its surroundings. We brought the one-dimensional lidar data into two-dimensional image data, then involved some object selection based on weak characteristics, such as the acceptable range radius of the object and the acceptable distance between two legs. The result of the process is a group of human candidates. We use a deep learning model with a pretrained mobilenetv3 model as the feature extractor to ensure whether the candidates are humans and a fully connected layer of neural networks as the classifier. We defined several scenarios in an indoor environment to test the performance of the proposed algorithm. The experiment results show the system's performance with a precision score of 0.902, a recall score of 0.782, and an F1 score of 0.837.",
  "keywords": [
    "Mobile Robot",
    "Lidar Sensor",
    "Object Selection",
    "Deep Learning",
    "MobileNet"
  ],
  "paper_institutions": [
    "Politeknik Elektronika Negeri Surabaya (PENS)"
  ],
  "output_language": "zh",
  "provider": "doubao",
  "model": "ark-code-latest",
  "prompt_template": "simple",
  "extracted_at": "2026-08-16T19:47:11.570715Z",
  "template_tag": "simple",
  "summary": "### 摘要\n该研究旨在为移动机器人在社会环境中实现基于激光雷达的人体检测，以支持社交感知导航。当前机器人在人类环境中工作时，需要感知人类位置以避免干扰，而传统视觉传感器易受光照影响，多层激光雷达成本高。本文提出一种轻量级方案：将单线2D激光雷达的一维距离数据转换为二维图像，通过形态学闭运算填补空洞，再利用轮廓检测和基于半径、腿间距的规则筛选出候选人腿候选。随后采用预训练的MobileNetV3作为特征提取器，配合一个单层256节点全连接网络作为分类器，以区分真实人体腿与其他物体。数据集在室内环境自动采集，生成10267张正样本和16383张负样本，按8:2划分训练集与验证集，训练100轮，训练准确率0.99、验证准确率0.98。在走廊和宽阔大厅两种真实场景中测试，系统累计真阳性431、假阳性47、假阴性120，最终精确率0.902、召回率0.782、F1分数0.837。局限包括无法检测单腿扫描情况，以及椅子腿等形状相似物体可能误检，未来建议加入物体距离参数以提升区分能力。核心机制是：将激光雷达极坐标数据转换为直角坐标并映射为图像，经闭运算、轮廓提取、规则筛选后输入深度神经网络分类。\n\n### 关键要点\n- 采用单线2D激光雷达（RPLidar A3）作为唯一感知传感器，成本低且广泛用于机器人导航。\n- 将一维距离数据通过三角函数（x=r cosγ, y=r sinγ）转换为二维坐标并绘制成图像。\n- 使用OpenCV的闭运算（先膨胀后腐蚀）修复激光雷达扫描间隔造成的图像空洞。\n- 通过轮廓检测提取候选区域，并依据人体腿平均半径（2-5像素）和双腿间最大距离（10像素）规则过滤。\n- 分类器使用预训练MobileNetV3作为特征提取器，后接单层256节点全连接神经网络作为分类器。\n- 训练数据集自动采集：正样本10267张（人腿），负样本16383张（非腿），按8:2分为训练和验证集。\n- 训练过程冻结MobileNetV3权重，训练100轮，训练准确率0.99、验证准确率0.98。\n- 真实测试在走廊和宽阔大厅两个场景进行，使用摄像头记录地面真值进行对比。\n- 实验累积结果：真阳性431、假阳性47、假阴性120，计算得精确率0.902、召回率0.782、F1分数0.837。\n- 系统局限：仅能检测双腿同时可见的情况，单腿时无法识别；椅子腿等在特定距离下可能被误判为人类腿。\n- 未来改进方向：在特征中加入物体距离参数，以更好区分相似形状但尺寸不同的物体。\n- 该方案可应用于社交机器人导航，使机器人能够感知并避开人类，提升社会接受度。"
}