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Accurate 3D point cloud map generation is a core task for various robotmissions or even for data-driven urban analysis. To do so, light detection andranging (LiDAR) sensor-based simultaneous localization and mapping (SLAM)technology have been elaborated. To compose a full SLAM system, many odometryand place recognition methods have independently been proposed in academia.However, they have hardly been integrated or too tightly combined so thatexchanging (upgrading) either single odometry or place recognition module isvery effort demanding. Recently, the performance of each module has beenimproved a lot, so it is necessary to build a SLAM system that can effectivelyintegrate them and easily replace them with the latest one. In this paper, werelease such a front-end agnostic LiDAR SLAM system, named SC-LiDAR-SLAM. Webuilt a complete SLAM system by designing it modular, and successfullyintegrating it with Scan Context++ and diverse existing opensource LiDARodometry methods to generate an accurate point cloud map

SC-LiDAR-SLAM: a Front-end Agnostic Versatile LiDAR SLAM System