{
  "paper_id": "416ba9614e33c5eda44df5202732aaf3",
  "paper_title": "Gaussian-Process-Based Real-Time Ground Segmentation for Autonomous Land Vehicles",
  "paper_authors": [
    "Tongtong Chen",
    "Bin Dai",
    "Ruili Wang",
    "Daxue Liu"
  ],
  "publication_date": "2013",
  "publication_venue": "Journal of Intelligent \\& Robotic Systems",
  "abstract": "Ground segmentation is a key component for Autonomous Land Vehicle (ALV) navigation in an outdoor environment. This paper presents a novel algorithm for real-time segmenting three-dimensional scans of various terrains. An individual terrain scan is represented as a circular polar grid map that is divided into a number of segments. A one-dimensional Gaussian Process (GP) regression with a non-stationary covariance function is used to distinguish the ground points or obstacles in each segment. The proposed approach splits a large-scale ground segmentation problem into many simple GP regression problems with lower complexity, and can then get a real-time performance while yielding acceptable ground segmentation results. In order to verify the effectiveness of our approach, experiments have been carried out both on a public dataset and the data collected by our own ALV in different outdoor scenes. Our approach has been compared with two previous ground segmentation techniques. The results show that our approach can get a better trade-off between computational time and accuracy. Thus, it can lead to successive object classification and local path planning in real time. Our approach has been successfully applied to our ALV, which won the championship in the 2011 Chinese Future Challenge in the city of Ordos.",
  "keywords": [
    "Autonomous land vehicle",
    "Ground segmentation",
    "Gaussian process",
    "3D point cloud"
  ],
  "paper_institutions": [],
  "output_language": "zh",
  "provider": "doubao",
  "model": "ark-code-latest",
  "prompt_template": "simple",
  "extracted_at": "2026-08-16T19:46:30.577241Z",
  "template_tag": "simple",
  "summary": "### 摘要\n本文提出一种基于高斯过程回归的实时地面分割算法，用于自主陆地车辆。算法将三维激光雷达扫描映射到圆形极坐标网格图，划分为多个扇形段，每个段内进行一维高斯过程回归，使用非平稳协方差函数以适应地面起伏。通过将大规模二维分割问题分解为多个低复杂度的回归问题，实现了实时性能。在公共数据集和自采数据上实验，与两种现有方法比较，取得了更好的时间与精度平衡。核心机制包括利用局部梯度自适应长度尺度，以及迭代种子选择。实验表明平均每帧处理时间约74.93毫秒，精度达到0.9767，并在2011年中国未来挑战赛中获得冠军。\n### 关键要点\n- 算法将三维点云映射到极坐标网格图，每个扇形段内使用一维高斯过程回归建模地面。\n- 采用非平稳协方差函数，长度尺度根据局部梯度自适应调整，以适应平坦和粗糙地形。\n- 通过迭代学习，从初始种子出发，逐步添加符合条件的地面点作为新种子，实现离群点剔除。\n- 在波士顿城市街道的公共数据集上，算法精度达到0.9767，高于线拟合方法的0.9651，略低于GP-INSAC的0.9784。\n- 算法误分类障碍物为地面的数量最少，相对更安全。\n- 在粗糙地形和斜坡上，算法优于线拟合方法，且比GP-INSAC快得多（平均74.93ms vs 678.77ms）。\n- 平均每帧处理时间74.93毫秒，满足实时要求（扫描周期100毫秒）。\n- 算法在2011年中国未来挑战赛中成功应用，自主完成10公里路线，获得冠军。\n- 局限性：对缓慢上升的障碍如楼梯踏步不敏感，可能误分类为地面。\n- 局限性：在陡坡场景中，由于没有初始种子，算法可能失效。\n- 算法将复杂问题分解为多个简单回归，显著提高了计算效率，但只能保持段内连续性，段间连续性不足。"
}