Aiming to address the issue of mapping failure resulting from unsmooth motion during SLAM (Simultaneous Localization and Mapping) performed by a quadruped robot, a tightly coupled SLAM algorithm that integrates LIDAR and IMU sensors is proposed in this paper. Firstly, the IMU information, after undergoing deviation correction, is utilized to remove point cloud distortion and serve as the initial value for point cloud registration. Subsequently, a registration algorithm based on Normal Distribution Transform (NDT) and sliding window is presented to ensure real-time positioning and accuracy. Then, an error function combining IMU and LIDAR is formulated using a factor graph, which iteratively optimizes position, attitude, and IMU deviation. Finally, loop closure detection based on Scan Context is introduced, and loop closure factors are incorporated into the factor graph to achieve effective mapping. An experimental platform is established to conduct accuracy and robustness comparison experiments. Results showed that the proposed algorithm significantly outperforms the LOAM algorithm, the NDT-based SLAM algorithm and the LeGO-LOAM algorithm in terms of positioning accuracy, with a reduction of 65.08%, 22.81%, and 37.14% in root mean square error, respectively. Moreover, the proposed algorithm exhibits superior robustness compared to LOAM, NDT-based SLAM and LeGO-LOAM.

中文翻译：

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一种用于四足机器人的紧耦合 LIDAR-IMU SLAM 方法

针对四足机器人SLAM（同步定位与建图）过程中运动不平滑导致建图失败的问题，提出一种集成LIDAR和IMU传感器的紧耦合SLAM算法。首先，利用IMU信息经过偏差校正后，去除点云畸变，作为点云配准的初始值。随后，提出了一种基于正态分布变换（NDT）和滑动窗口的配准算法，以确保定位的实时性和准确性。然后，使用因子图制定结合 IMU 和 LIDAR 的误差函数，迭代优化位置、姿态和 IMU 偏差。最后，引入基于Scan Context的闭环检测，将闭环因子纳入因子图中，实现有效映射。搭建实验平台，进行准确性和鲁棒性对比实验。结果表明，该算法在定位精度方面显着优于LOAM算法、基于NDT的SLAM算法和LeGO-LOAM算法，均方根误差分别降低了65.08%、22.81%和37.14% 。此外，与 LOAM、基于 NDT 的 SLAM 和 LeGO-LOAM 相比，所提出的算法表现出卓越的鲁棒性。