Abstract
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{this https URL}{this https URL}.
Abstract (translated)
集成多个激光雷达传感器可以显著增强机器人对周围环境的感知,使其能够捕捉适当的测量,实现同时定位和地图构建(SLAM)。事实上,固态激光雷达在机器人应用中可以提供高分辨率,而传统旋转激光雷达的成本较低。然而,它们的的视角减少限制了性能,特别是在室内。在本文中,我们提出了一种紧密耦合的多模态多激光雷达惯性SLAM系统,以进行测量和地图构建任务。利用固态和旋转激光雷达的优点,以及内置惯性测量单元(IMU),我们在各种挑战性的室内环境中(例如小型无特征房间)实现了 robust 和低漂移的自估计值,以及高分辨率地图。首先,我们使用空间时间校准模块对齐传感器之间的外部参数。然后,我们从激光雷达数据中提取了两个组特征点,包括边缘和平面点。接下来,使用预先集成的IMU数据,应用一个无扭曲模块对激光雷达点云数据进行处理。最后,无扭曲点云合并成一个点云,并通过滑动窗口based优化模块进行处理。从广泛的实验结果中,我们的方法表现出与最先进的旋转激光雷达 only 或固态激光雷达 only 的SLAM系统在多种环境中的竞争性表现。更多结果、代码和数据可查阅 href{this https URL}{this https URL}。
URL
https://arxiv.org/abs/2303.02684
