Numerous researchers have conducted studies to achieve fast and robust ground-optimized LiDAR odometry methods for terrestrial mobile platforms. In particular, ground-optimized LiDAR odometry usually employs ground segmentation as a preprocessing method. This is because most of the points in a 3D point cloud captured by a 3D LiDAR sensor on a terrestrial platform are from the ground. However, the effect of the performance of ground segmentation on LiDAR odometry is still not closely examined. In this paper, a robust ground-optimized LiDAR odometry framework is proposed to facilitate the study to check the effect of ground segmentation on LiDAR SLAM based on the state-of-the-art (SOTA) method. By using our proposed odometry framework, it is easy and straightforward to test whether ground segmentation algorithms help extract well-described features and thus improve SLAM performance. In addition, by leveraging the SOTA ground segmentation method called Patchwork, which shows robust ground segmentation even in complex and uneven urban environments with little performance perturbation, a novel ground-optimized LiDAR odometry is proposed, called PaGO-LOAM. The methods were tested using the KITTI odometry dataset. \textit{PaGO-LOAM} shows robust and accurate performance compared with the baseline method. Our code is available at https://github.com/url-kaist/AlterGround-LeGO-LOAM.
翻译:许多研究人员进行了研究,以实现地面移动平台的快速和稳健地优化LIDARodology方法。特别是,地面优化的LIDAR Odorization通常使用地面分割法作为预处理方法,这是因为地面平台上由3DLIDAR传感器捕获的3D点云中的大部分点来自地面。然而,地面分割法对LIDAR的测量法的影响仍未得到密切审查。本文件提议了一个强有力的地面优化LIDAR Ododorization框架,以便利于根据“状态-艺术”(SOTA)方法检查地面分割法对LIDAR SALM的影响。通过使用我们提议的“3DLIDA” 测量框架,可以很容易和直接测试地面分割法是否有助于提取清晰的特征,从而改善SLAMM的性能。此外,利用SOTA的地面分割法,这显示即使在复杂且不均匀的城市环境中,也显示地面分割的稳健固化地面分割法,一种创新的地面-ARSLAM-GOSAR-SAR-SAR-SDSD 数据是使用我们现有的地面测量方法。