Multi-beam LiDAR sensors are increasingly used in robotics, particularly for autonomous cars for localization and perception tasks. However, perception is closely linked to the localization task and the robot's ability to build a fine map of its environment. For this, we propose a new real-time LiDAR odometry method called CT-ICP, as well as a complete SLAM with loop closure. The principle of CT-ICP is to use an elastic formulation of the trajectory, with a continuity of poses intra-scan and discontinuity between scans, to be more robust to high frequencies in the movements of the sensor. The registration is based on scan-to-map with a dense point cloud as map structured in sparse voxels to operate in real time. At the same time, a fast method of loop closure detection using elevation images and an optimization of poses by graph allows to obtain a complete SLAM purely on LiDAR. To show the robustness of the method, we tested it on seven datasets: KITTI, KITTI-raw, KITTI-360, KITTI-CARLA, ParisLuco, Newer College, and NCLT in driving and high-frequency motion scenarios. The CT-ICP odometry is implemented in C++ and available online. The loop detection and pose graph optimization is in the framework pyLiDAR-SLAM in Python and also available online. CT-ICP is currently first, among those giving access to a public code, on the KITTI odometry leaderboard, with an average Relative Translation Error (RTE) of 0.59% and an average time per scan of 60ms on a CPU with a single thread.

翻译：多波成 LiDAR 传感器越来越多地用于机器人, 特别是用于本地化和感知任务的自动汽车。 然而, 感知与本地化任务和机器人构建其环境精密地图的能力密切相关。 为此, 我们提出一种新的实时LiDAR odo测量方法, 称为CT- ICP, 以及完整的循环闭合的 SLAM。 CT- ICP 原则是使用轨迹的弹性配方, 使扫描在扫描之间具有连续性和不连续性, 在传感器移动中对高频率更加强大。 注册的基础是扫描到图, 厚点云, 以稀薄的 voxel 为结构, 实时运行。 同时, 快速的循环闭合检测方法, 使用高平面图像和图像优化, 完全在LDAR。 为了显示该方法的稳健性, 我们在七个数据集上测试了它: KITTI, KITTI-raw, KITTI-360, KITTI- CARD 和 KITTI- CAROLA 的高级点云云云云云, 运行中, 高级的CLILI 和SULI 的Siral- trial oral oral oral oral orlal oral orlal orlal orlal orlal oral oral orl orlal orlal orldal oral orldal orlal orlal orlal orlal orl orld orldal orlal 。 在 CLI orld orld 和CLLI oral orld ral oral oral ral oral orlal oral oral oral orlal ral roldal rolal 。 在 CLI