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A New Grid Map Construction Method for Autonomous Vehicles

机译：一种新的无人驾驶汽车栅格地图构建方法

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Grids map provide a simple but accurate way of understanding surroundings which means they could play a vital role in methods of environment perception. A new grid map construction approach to environment perception aimed at unmanned ground vehicles is proposed in this paper. First, the pose of the raw point cloud from the LiDAR system (Velodyne HDL-32E) is aligned by introducing data from IMU. Then, the RANSAC algorithm is utilized to remove the ground part of the point cloud and a grid map is established using octrees. The probability of occupancy grid map is updated based on data fusion with Bayesian inference and the Dezert–Smarandache theory combination rule. Finally, a cluster analysis is performed and moving objects are detected on the grid map, in order to facilitate obstacle detection and selection of the accessible road area. Experimental results show that the resulting grid map based on octrees and data fusion can be reliably applied to vehicle perception and that the approach is highly practicable.
机译：网格图提供了一种简单而准确的理解周围环境的方式，这意味着它们可以在环境感知方法中发挥至关重要的作用。提出了一种针对无人地面车辆的环境感知新的网格图构建方法。首先，通过引入IMU的数据来调整来自LiDAR系统（Velodyne HDL-32E）的原始点云的姿态。然后，使用RANSAC算法去除点云的地面部分，并使用八叉树建立网格图。使用贝叶斯推断和Dezert-Smarandache理论组合规则进行数据融合的基础上，更新占用栅格图的概率。最后，进行聚类分析并在栅格地图上检测运动对象，以便于障碍物检测和可通行道路区域的选择。实验结果表明，基于八叉树和数据融合的结果网格图可以可靠地应用于车辆感知，并且该方法是高度可行的。

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