Integrating multiple LiDAR sensors can significantly enhance a robot's perception of the environment, enabling it to capture adequate measurements for simultaneous localization and mapping (SLAM). Indeed, solid-state LiDARs can bring in high resolution at a low cost to traditional spinning LiDARs in robotic applications. However, their reduced field of view (FoV) limits performance, particularly indoors. In this paper, we propose a tightly-coupled multi-modal multi-LiDAR-inertial SLAM system for surveying and mapping tasks. By taking advantage of both solid-state and spinnings LiDARs, and built-in inertial measurement units (IMU), we achieve both robust and low-drift ego-estimation as well as high-resolution maps in diverse challenging indoor environments (e.g., small, featureless rooms). First, we use spatial-temporal calibration modules to align the timestamp and calibrate extrinsic parameters between sensors. Then, we extract two groups of feature points including edge and plane points, from LiDAR data. Next, with pre-integrated IMU data, an undistortion module is applied to the LiDAR point cloud data. Finally, the undistorted point clouds are merged into one point cloud and processed with a sliding window based optimization module. From extensive experiment results, our method shows competitive performance with state-of-the-art spinning-LiDAR-only or solid-state-LiDAR-only SLAM systems in diverse environments. More results, code, and dataset can be found at \href{https://github.com/TIERS/multi-modal-loam}{https://github.com/TIERS/multi-modal-loam}.

翻译：集成多台激光雷达传感器可显著增强机器人对环境的感知能力，使其能够为同时定位与建图（SLAM）获取充足的测量数据。在机器人应用中，固态激光雷达能以低成本为传统旋转式激光雷达提供高分辨率。然而，其视场角（FoV）的减小限制了性能表现，尤其在室内环境中。本文提出一种面向测绘任务的紧耦合多模态多激光雷达-惯性SLAM系统。通过整合固态与旋转式激光雷达的优势及内置惯性测量单元（IMU），我们能够在多样化的挑战性室内环境（如狭小、无特征的房间）中实现鲁棒且低漂移的自我状态估计，同时生成高分辨率地图。首先，采用时空标定模块对齐传感器间的时间戳并标定外参。然后，从激光雷达数据中提取包括边缘点和平面点在内的两组特征点。接着，利用预积分的IMU数据对激光雷达点云进行去畸变处理。最后，将去畸变后的点云融合为单一云数据，并通过基于滑动窗口的优化模块进行处理。大量实验结果表明，本方法在多种环境下与仅采用旋转式或固态激光雷达的最先进SLAM系统相比具有竞争力的性能。更多结果、代码及数据集请访问：\href{https://github.com/TIERS/multi-modal-loam}{https://github.com/TIERS/multi-modal-loam}