基于LiDAR定向子图像的自动驾驶地图之间的途径转移模块.
Mohammad Aldibaja1, Ryo Yanase1, Naoki Suganuma1
1The Advanced Mobility Research Institute, Kanazawa University, Kanazawa 920-1192, Japan.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
这项研究引入了一个新的框架,用于在地图之间准确地转移车道图,这对于自动驾驶系统至关重要. 该方法确保了可靠的车道图形传输,即使在复杂的道路结构和不同的地图准确度.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 自主驾驶系统 自主驾驶系统
背景情况:
- 车道图对于自动驾驶车辆导航至关重要,为定位和路径规划提供道路语义.
- 由于更新和多种定位系统 (GNSS/INS-RTK,DR,SLAM),保持拓地图的准确全球位置是一个挑战.
- 在地图之间准确地传输车道图表对于车道和地标的一致语义表示至关重要.
研究的目的:
- 提出基于LiDAR强度道路表面的车道图形的独特图像域传输框架.
- 为了应对复杂道路结构中实施车道图转移的挑战.
- 确保安全准确地将车道图表传输到具有潜在不同的全球准确度和生成方法的地图之间.
主要方法:
- 目标地图中的道路表面分解为全球坐标系中的X,Y和YawID的方向子图像.
- 使用XYID来识别目标地图和参考地图之间的共同区域.
- 使用Yaw ID重建车辆轨迹并确定参考地图中的相关车道图表以匹配和转移.
主要成果:
- 拟议的框架成功地在地图之间安全准确地传输了车道图.
- 实验结果表明,在各种道路结构,驾驶场景和地图生成方法中具有强度.
- 框架的可靠性得到验证,不管地图的全球准确性有何变化.
结论:
- 开发的框架为传输车道图提供了强大的解决方案,提高了自动驾驶系统的可靠性.
- 基于LiDAR强度路面的图像域方法有效地处理复杂的道路语义和地图差异.
- 这种方法确保了一致和准确的车道图表表示,这对于安全的自动驾驶机动至关重要.
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