Precise positioning is the basic condition for intelligent vehicles to complete perception, decision making and control tasks. In response to this challenge, in this article, lidar simultaneous localization and mapping (SLAM) is taken as the research object, and a SLAM system is designed that integrates motion compensation and ground information removal functions, and can construct a real-time environment map and determine its own position on the map while the vehicle is driving. A loop-closure detection method with a multiresolution point cloud histogram mode is proposed, which can effectively detect whether the vehicle passes through the same position and perform optimization to obtain globally consistent pose and map information in the urban conditions with more driving loops. We conduct experiments on the well-known KITTI dataset and compare the results with those of state-of-the-art systems. The experiments confirm that the lidar SLAM system designed in this article can provide accurate and effective positioning information for intelligent vehicles. The proposed loop-closure detection algorithm has an excellent real-time performance and accuracy, which can guarantee the long-term driving operation of these vehicles.

中文翻译：

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在智能车辆的激光雷达里程计和测绘中使用多分辨率点云直方图模式进行环路闭合检测

精准定位是智能汽车完成感知、决策和控制任务的基本条件。针对这一挑战，本文以激光雷达同步定位与建图（SLAM）为研究对象，设计了一种集成运动补偿和地面信息去除功能，可构建实时环境地图的SLAM系统并在车辆行驶时确定自己在地图上的位置。提出了一种多分辨率点云直方图模式的闭环检测方法，能够有效检测车辆是否经过同一位置并进行优化，在行驶回路较多的城市条件下获得全局一致的位姿和地图信息。我们在著名的 KITTI 数据集上进行实验，并将结果与​​最先进系统的结果进行比较。实验证实，本文设计的激光雷达SLAM系统可以为智能车辆提供准确有效的定位信息。所提出的闭环检测算法具有良好的实时性和准确性，可以保证这些车辆的长期行驶运行。