This paper focuses on the Light Detection and Ranging (LiDAR)–Inertial Measurement Unit (IMU) simultaneous localization and mapping (SLAM) problem: How to fuse the sensor measurement from the LiDAR and IMU to online estimate robot's poses and build a consistent map of the environment. This paper presents LTA‐OM: an efficient, robust, and accurate LiDAR SLAM system. Employing fast direct LiDAR‐inertial odometry (FAST‐LIO2) and Stable Triangle Descriptor as LiDAR–IMU odometry and the loop detection method, respectively, LTA‐OM is implemented to be functionally complete, including loop detection and correction, false‐positive loop closure rejection, long‐term association (LTA) mapping, and multisession localization and mapping. One novelty of this paper is the real‐time LTA mapping, which exploits the direct scan‐to‐map registration of FAST‐LIO2 and employs the corrected history map to provide direct global constraints to the LIO mapping process. LTA mapping also has the notable advantage of achieving drift‐free odometry at revisit places. Besides, a multisession mode is designed to allow the user to store the current session's results, including the corrected map points, optimized odometry, and descriptor database for future sessions. The benefits of this mode are additional accuracy improvement and consistent map stitching, which is helpful for life‐long mapping. Furthermore, LTA‐OM has valuable features for robot control and path planning, including high‐frequency and real‐time odometry, driftless odometry at revisit places, and fast loop closing convergence. LTA‐OM is versatile as it is applicable to both multiline spinning and solid‐state LiDARs, mobile robots and handheld platforms. In experiments, we exhaustively benchmark LTA‐OM and other state‐of‐the‐art LiDAR systems with 18 data sequences. The results show that LTA‐OM steadily outperforms other systems regarding trajectory accuracy, map consistency, and time consumption. The robustness of LTA‐OM is validated in a challenging scene—a multilevel building having similar structures at different levels. To demonstrate our system, we created a video which can be found on https://youtu.be/DVwppEKlKps.

中文翻译：

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LTA-OM：长期关联 LiDAR-IMU 里程计和测绘

本文重点讨论光探测和测距 (LiDAR) – 惯性测量单元 (IMU) 同时定位和建图 (SLAM) 问题：如何将 LiDAR 和 IMU 的传感器测量融合到在线估计机器人的位姿并构建一致的地图环境。本文介绍了 LTA-OM：一种高效、稳健且准确的 LiDAR SLAM 系统。分别采用快速直接LiDAR惯性里程计（FAST-LIO2）和稳定三角形描述符作为LiDAR-IMU里程计和环路检测方法，LTA-OM实现功能完整，包括环路检测和校正、假阳性环路闭合拒绝、长期关联（LTA）映射以及多会话定位和映射。本文的一项新颖之处是实时 LTA 映射，它利用 FAST-LIO2 的直接扫描到映射注册，并利用校正后的历史映射为 LIO 映射过程提供直接的全局约束。 LTA 测绘还具有在重访地点实现无漂移里程计的显着优势。此外，多会话模式的设计允许用户存储当前会话的结果，包括校正的地图点、优化的里程计以及未来会话的描述符数据库。这种模式的好处是额外的精度提高和一致的地图拼接，这有助于终身制图。此外，LTA-OM 对于机器人控制和路径规划具有宝贵的功能，包括高频实时里程计、重访地点的无漂移里程计以及快速闭环收敛。 LTA-OM 用途广泛，适用于多线旋转激光雷达和固态激光雷达、移动机器人和手持平台。在实验中，我们使用 18 个数据序列对 LTA-OM 和其他最先进的 LiDAR 系统进行了详尽的基准测试。结果表明，LTA-OM 在轨迹精度、地图一致性和时间消耗方面稳步优于其他系统。 LTA-OM 的稳健性在一个具有挑战性的场景中得到了验证——一座不同楼层具有相似结构的多层建筑。为了演示我们的系统，我们创建了一个视频，可以在https://youtu.be/DVwppEKlKps。