在城市环境中实现自动驾驶的概率语义映射
Hengyuan Zhang1, Shashank Venkatramani1, David Paz1
1Autonomous Vehicle Laboratory, Contextual Robotics Institute, University of California San Diego, La Jolla, CA 92093, USA.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究提出了一种创新的方法,通过将点云数据与图像融合,为自动驾驶汽车创建高清 (HD) 地图. 这种方法自动识别静态地标,降低高清地图维护的成本和复杂性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 高清地图对于自动驾驶汽车技术至关重要.
- 创建和维护高清地图的高成本是一个重大挑战.
- 目前的方法严重依赖于昂贵的手动更新的地图数据.
研究的目的:
- 开发一种用于生成语义高清地图的自动化方法.
- 为了降低与高清地图创建和维护相关的成本和精力.
- 将预先构建的点云数据与2D图像进行融合,用于地标识别.
主要方法:
- 使用二维图像的语义细分来识别道路,人行道和人行道交叉等特征.
- 从图像中关联语义标签与预先构建的点云地图中的点.
- 采用一个混矩阵公式来生成一个概率的鸟视图语义地图.
- 在机器人操作系统 (ROS) 中实现管道.
主要成果:
- 在城市环境中成功生成了带有道路特征的语义地图.
- 该方法准确识别静态地标,提供丰富的环境背景.
- 展示了基于图像的语义细分和点云数据的融合.
结论:
- 拟议的方法为生成和维护语义高清地图提供了具有成本效益的解决方案.
- 生成的地图增强下游任务,如轨迹生成和意图预测.
- 这种方法有可能在高清地图中自动生成语义特征.
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