{
  "paper_id": "7410241f1bf0b8bf4ca00dd8c02d81e0",
  "paper_title": "2D vs. 3D LiDAR-based Person Detection on Mobile Robots",
  "paper_authors": [
    "Dan Jia",
    "Alexander Hermans",
    "Bastian Leibe"
  ],
  "publication_date": "",
  "publication_venue": "2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
  "abstract": "Person detection is a crucial task for mobile robots navigating in human-populated environments. LiDAR sensors are promising for this task, thanks to their accurate depth measurements and large field of view. Two types of LiDAR sensors exist: the 2D LiDAR sensors, which scan a single plane, and the 3D LiDAR sensors, which scan multiple planes, thus forming a volume. How do they compare for the task of person detection? To answer this, we conduct a series of experiments, using the public, large-scale JackRabbit dataset and the state-of-the-art 2D and 3D LiDAR-based person detectors (DR-SPAAM and CenterPoint respectively). Our experiments include multiple aspects, ranging from the basic performance and speed comparison, to more detailed analysis on localization accuracy and robustness against distance and scene clutter. The insights from these experiments highlight the strengths and weaknesses of 2D and 3D LiDAR sensors as sources for person detection, and are especially valuable for designing mobile robots that will operate in close proximity to surrounding humans (e.g. service or social robot).",
  "keywords": [],
  "paper_institutions": [
    "RWTH Aachen University"
  ],
  "output_language": "zh",
  "provider": "doubao",
  "model": "ark-code-latest",
  "prompt_template": "simple",
  "extracted_at": "2026-08-16T19:46:51.547894Z",
  "template_tag": "simple",
  "summary": "### 摘要\n本论文针对移动机器人在人类密集环境中的行人检测任务，系统比较了2D和3D LiDAR传感器的性能差异。研究背景是现有传感器选择对机器人设计至关重要，而以往缺乏直接对比。我们采用当前最先进的3D检测器CenterPoint和2D检测器DR-SPAAM，在公开的JackRabbit数据集上进行实验，涵盖检测精度、推理速度、定位准确性、距离鲁棒性和拥挤场景鲁棒性等多个方面。结果表明，在仅考虑传感器可见的行人时，两种传感器性能相当，但3D LiDAR整体更鲁棒，而2D LiDAR在速度上显著占优，适合计算资源受限的移动机器人。实验中，CenterPoint通过细粒度体素化和nuScenes预训练达到最佳性能，DR-SPAAM则通过降采样实现更快推理。核心机制上，CenterPoint利用体素化点云在鸟瞰图上生成热图来预测中心，DR-SPAAM采用一维CNN对极坐标距离数据进行分类和回归。这些发现为机器人在近距离交互场景下的传感器选型提供了重要指导，并指出集成两种检测器可能进一步提升检测可靠性。\n### 关键要点\n- 使用CenterPoint（3D）和DR-SPAAM（2D）分别代表两类LiDAR行人检测器进行对比。\n- 在JackRabbit数据集上进行实验，该数据集包含室内外校园场景，具有3D和2D LiDAR同步数据。\n- 在默认评估下，CenterPoint的AP_box比DR-SPAAM高31.9%，但仅考虑2D可见行人时差距缩小至9.2%。\n- 当仅关注行人质心位置时，DR-SPAAM和CenterPoint的AP_centroid仅相差3.3%。\n- DR-SPAAM的推理速度约为CenterPoint的两倍，在桌面、笔记本和嵌入式平台均如此。\n- 通过使用更细的体素化（0.05m）和nuScenes预训练，CenterPoint在JRDB上的AP_box从26.3提升至70.0。\n- 定位精度实验表明，CenterPoint在小于0.3m的关联半径下性能饱和更快，定位略优。\n- 在距离评估中，默认评估下CenterPoint性能更稳定，但2D可见评估下两者相似。\n- 人群密集度对两种检测器的影响相近，性能随群体大小增大略有下降。\n- 约50.5%的行人被两种检测器同时检测到，28.0%仅被CenterPoint检测到，3.8%仅被DR-SPAAM检测到。\n- DR-SPAAM通过扫描降采样可在Jetson AGX Xavier上以26.6 FPS运行，仅损失1.4% AP。\n- 2D LiDAR传感器较轻便、便宜且速度快，适合简单任务；3D LiDAR更通用但成本高，两者互补。"
}