Abstract: In recent years, the field of unmanned driving has become a hot topic of widespread concern, and the same time, simultaneous localization and mapping (SLAM) technology is the basis of high-precision map creation and unmanned vehicle navigation, and the current mainstream laser SLAM algorithm can basically meet the needs of applications, but there is still a drift problem in the process of large-scale scene mapping, and there is still room for improvement in algorithm lightweight and real-time. In this paper, the simultaneous localization and mapping algorithm of lidar and inertial measurement unit (IMU) tightly coupled is mainly studied, and the front-end part mainly removes distortion and feature extraction of laser point cloud data, and the back-end uses factor map to fuse IMU pre-integration factor, laser odometry factor and loopback detection factor for fusion pose output. In order to improve the real-time performance of the algorithm, this paper uses the iKD-Tree data structure to maintain a local map, and uses the Fast-generalized iterative closest point (GICP) algorithm to solve the loopback detection pose transformation. The test of Kitti's public dataset shows that the proposed algorithm improves the real-time and robustness of the algorithm while ensuring accuracy.

Key words: Lidar; Factor graph optimization; IMU; Tight coupled; SLAM algorithm

摘要： 近年来，无人驾驶领域成为广泛关注的热点方向，同时定位与地图构建（SLAM）技 术是高精地图创建和无人车辆导航的基础，当下主流的激光 SLAM 算法基本能够满足应用的 需求，但是在大范围场景建图的过程中仍然存在漂移的问题，且算法轻量化以及实时性方面 依旧有着改进的空间。文章主要进行了激光雷达和惯性测量单元（IMU）紧耦合的同时定位 与建图算法研究，前端部分主要对激光点云数据进行了去除畸变、特征提取，后端使用因子 图融合 IMU 预积分因子、激光里程计因子和回环检测因子进行融合位姿输出。为了提高算法 的实时性，文章使用 iKD-Tree 数据结构维护了一个局部地图，并使用 Fast-GICP 算法求解回 环检测位姿变换。在 Kitti 公开数据集的测试表明，改算法在保证精度的同时提高了算法的实 时性和鲁棒性。

关键词: 激光雷达；因子图优化；IMU；紧耦合；SLAM 算法