The ubiquitous planes and structural consistency are the most apparent features of indoor multi-story Buildings compared with outdoor environments. In this paper, we propose a tightly coupled LiDAR-Inertial 3D SLAM framework with plane features for the multi-story building. The framework we proposed is mainly composed of three parts: tightly coupled LiDAR-Inertial odometry, extraction of representative planes of the structure, and factor graph optimization. By building a local map and inertial measurement unit (IMU) pre-integration, we get LiDAR scan-to-local-map matching and IMU measurements, respectively. Minimize the joint cost function to obtain the LiDAR-Inertial odometry information. Once a new keyframe is added to the graph, all the planes of this keyframe that can represent structural features are extracted to find the constraint between different poses and stories. A keyframe-based factor graph is conducted with the constraint of planes, and LiDAR-Inertial odometry for keyframe poses refinement. The experimental results show that our algorithm has outstanding performance in accuracy compared with the state-of-the-art algorithms.
翻译:与室外环境相比,无处不在的平面和结构一致性是室内多层建筑与室外环境最明显的特征。 在本文中, 我们建议使用一个与多层建筑平面特征相连接的、 紧凑的LiDAR- Instertial 3D SLAM 框架。 我们提出的框架主要由三个部分组成: 紧密结合的LiDAR- Intertial odology, 结构中具有代表性的平面的提取和要素图形优化。 通过建立本地地图和惯性测量单位( IMU) 的整合前, 我们分别获得LiDAR 扫描到本地映射匹配和IMU 测量。 最大限度地减少联合成本功能以获取LiDAR- Intertial odography 信息。 一旦在图形中添加了一个新的键框架, 代表结构特征的所有关键框架的平面都将被提取出来, 以找到不同形状和故事之间的制约。 基于关键框架的因子图在飞机的制约下进行操作, 以及 LiDAR- Intortial odorial 度测量关键框架的测量将进行精细化。 实验结果显示我们的算与状态相比, 我们的精确性表现是突出的。