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A Multi-Environment Freespace Detection Method Based on Range Scale Map

delete2025-07-01
delete0
PRE
AI
S
Siyuan Shao
K
Kunyang Wu
G
Genyuan Xing
刘
刘洋 (Yang Liu)
张
张冠宇 (Guanyu Zhang)
DOI：10.1109/TITS.2025.3568165delete
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摘要

摘要

En 中文
精确的自由空间检测对于确保自动驾驶车辆的安全运行至关重要。然而，创建多场景数据集可能具有挑战性。主流研究主要关注城市环境中的驾驶场景，而忽略了其他类型的道路环境。这导致当前自由空间检测方法的应用环境受限。本文提出了一种自适应环境尺度的2D图像空间自由空间检测方法。该方法不需要数据标注，并且具有更好的环境适应性。其核心思想是利用相机投影关系，将3D空间中的固定点云尺度自适应地映射到2D空间中的像素尺度，以获得精细的环境梯度。然后设计搜索规则以在2D空间中标注自由空间。在两个公开数据集（城市和野外）上的实验分别达到了92.50%和89.09%的F1分数。在结构化和非结构化环境中，所提出的方法均表现出比当前最先进方法更高的精度和更低的误检率。
Keyword:
Freespace
range scale map
multi-environment
point cloud

期刊

IEEE Transactions on Intelligent Transportation Systems 封面图
IEEE Transactions on Intelligent Transportation Systems
IF:
8.4
论文数:
9.5K
被引数:
6.3W

机构

J
Jilin University
学者数:
8.7W
论文数: 5.6W
被引数: 8.9K
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