论文标题

激光惯性3D大满贯,对多层建筑的平面限制

LiDAR-Inertial 3D SLAM with Plane Constraint for Multi-story Building

论文作者

Zhang, Jiashi, Zhang, Chengyang, Wu, Jun, Jin, Jianxiang, Zhu, Qiuguo

论文摘要

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的最明显特征。在本文中,我们提出了一个紧密耦合的激光惯性3D大满贯框架,并具有用于多层建筑的平面特征。我们提出的框架主要由三个部分组成:紧密耦合的激光惯性轴测图,结构的代表平面的提取以及因子图优化。通过构建本地地图和惯性测量单元(IMU)预一体化,我们分别获得LIDAR扫描到单位图匹配和IMU测量值。最小化关节成本函数以获取激光惯性的进程信息。一旦将新的密钥帧添加到图表中,可以提取可以提取代表结构特征的所有键框架的平面,以找到不同姿势和故事之间的约束。基于钥匙帧的因子图是通过平面约束进行的,而Keyframe构图的LIDAR惯性探光仪则进行了细化。实验结果表明,与最先进的算法相比,我们的算法在准确性方面具有出色的性能。

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.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源