LIDAR Graph SLAM based Autonomous Vehicle Maps using XY and Yaw Dead-Reckoning Measurements
LIDAR Graph SLAM based Autonomous Vehicle Maps using XY and Yaw Dead-Reckoning Measurements
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DOI：
10.1109/icma52036.2021.9512700
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发表时间：
2021-08
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影响因子：
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通讯作者：
Mohammad Aldibaja;Reo Yanase;N. Suganuma;Takahiro Furuya;Akitaka Oko
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作者：
Mohammad Aldibaja;Reo Yanase;N. Suganuma;Takahiro Furuya;Akitaka Oko
用于生成精确LIDAR地图的强大Graph Slam（GS）框架仍然是自动驾驶汽车玩家的一个挑战性需求。这是因为LIDAR 3D点云的稀疏性导致x、y和偏航方向上的相对位置误差的错误补偿。我们处理这个缺点，提出了一个独特的GS策略来处理图像域中的相对位置误差。这是通过使用“节点策略”将车辆轨迹转换为2D灰度图像来实现的，以在绝对坐标系（ACS）中对具有适当标识的密集环境表示进行编码。因此，采用相位相关和傅里叶梅林变换来非迭代地估计环路闭合区域图像域中节点之间的相对位置。这些估计被分配到一组边中，这些边构成地图中节点之间的关系。设计了两个单独的成本函数，以利用这些边来优化节点的偏航角，然后优化ACS中的x，y位置。建议的GS框架已在东京的高层建筑，密集的树木和纵向桥梁的挑战性环境中进行了测试。实验结果验证了基于航位推算测量生成精确地图的鲁棒性，并在关键道路结构中优于GNSS/INS-RTK地图。
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.