Accurate 3D point cloud map generation is a core task for various robot missions or even for data-driven urban analysis. To do so, light detection and ranging (LiDAR) sensor-based simultaneous localization and mapping (SLAM) technology have been elaborated. To compose a full SLAM system, many odometry and place recognition methods have independently been proposed in academia. However, they have hardly been integrated or too tightly combined so that exchanging (upgrading) either single odometry or place recognition module is very effort demanding. Recently, the performance of each module has been improved a lot, so it is necessary to build a SLAM system that can effectively integrate them and easily replace them with the latest one. In this paper, we release such a front-end agnostic LiDAR SLAM system, named SC-LiDAR-SLAM. We built a complete SLAM system by designing it modular, and successfully integrating it with Scan Context++ and diverse existing opensource LiDAR odometry methods to generate an accurate point cloud map
翻译：精确的 3D 点云图生成是各种机器人任务的核心任务,甚至数据驱动的城市分析也是3D 点云图生成的一项核心任务。 为此,已经开发了光探测和测距(LiDAR) 基于传感器的光探测和测距(LiDAR)同步同步定位和绘图(SLAM)技术。要形成完整的 SLAM 系统,学术界已经独立地提出了许多odo量度和地点识别方法。然而,它们几乎没有被整合或过于紧密地结合,因此,交换(升级)单一的odo测量或地点识别模块非常困难。最近,每个模块的性能都得到了很大的改进,因此有必要建立一个能够有效地整合这些模块的 SLAM 系统,并方便地用最新的系统取代它们。在本文件中,我们发布了这样一个名为 SC- LiDAR- SLAM 前端的LDAR SLAM 系统。 我们设计了一个完整的 SLAM 系统, 设计了模块, 成功地将它与扫描环境+和多种现有开源LiDAR odography 方法结合起来,以生成准确的云图。
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