Mohammad Aldibaja, Reo Yanase, N. Suganuma, Takahiro Furuya, Akitaka Oko
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引用次数: 1
Abstract
A robust Graph Slam (GS) framework for generating precise LIDAR maps is still a challenging demand for autonomous vehicle players. This is because of the sparsity of LIDAR 3D point clouds that leads to wrong compensations of relative position errors in x, y and yaw directions. We handle this drawback by proposing a unique GS strategy to process the relative position errors in the image domain. This is achieved by converting the vehicle trajectories into 2D grayscale images using “node strategy” to encode dense environmental representations with proper identifications in Absolute Coordinate System (ACS). Accordingly, Phase Correlation and Fourier Mellin Transform are employed to non-iteratively estimate the relative positions between nodes in the image domain at loop closure areas. These estimations are assigned into a set of edges that constitute the relationships between nodes in the map. Two individual cost functions are designed to utilize these edges in optimizing the nodes' yaw angles and then x, y positions in ACS. The proposed GS framework has been tested in a challenging environment of high buildings, dense trees and longitudinal bridge in Tokyo. The experimental results have verified the robustness to generate precise maps based on Dead Reckoning measurements and outperform GNSS/INS-RTK map in critical road structures.
对于自动驾驶汽车玩家来说,一个用于生成精确激光雷达地图的强大的Graph Slam (GS)框架仍然是一个具有挑战性的需求。这是因为LIDAR 3D点云的稀疏性导致在x、y和偏航方向上的相对位置误差补偿错误。我们提出了一种独特的GS策略来处理图像域的相对位置误差,从而解决了这一缺点。这是通过使用“节点策略”将车辆轨迹转换为二维灰度图像来实现的,在绝对坐标系统(ACS)中使用适当的识别编码密集的环境表示。因此,采用相位相关和傅里叶梅林变换非迭代地估计环路闭合区域图像域节点之间的相对位置。这些估计值被分配到一组边缘中,这些边缘构成了地图中节点之间的关系。设计了两个单独的成本函数来利用这些边来优化节点的偏航角,然后在ACS中优化x, y位置。提议的GS框架已经在东京的高层建筑、茂密的树木和纵向桥梁的具有挑战性的环境中进行了测试。实验结果验证了基于航位估算生成精确地图的鲁棒性,在关键道路结构上优于GNSS/INS-RTK地图。