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

TL;DR

This paper introduces a LiDAR-Inertial SLAM framework utilizing plane features to improve multi-story building mapping accuracy, leveraging structural regularities for enhanced pose estimation.

Contribution

It presents a novel tightly coupled LiDAR-Inertial SLAM method with plane constraints specifically designed for multi-story buildings, integrating structural features into the optimization process.

Findings

Outperforms state-of-the-art algorithms in accuracy

Effectively extracts structural planes for pose constraints

Improves multi-story building mapping robustness

Abstract

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.