# BA-CLM: A Globally Consistent 3D LiDAR Mapping Based on Bundle Adjustment Cost Factors

## TL;DR

This paper introduces a new 3D LiDAR mapping framework that improves global consistency and accuracy in robot navigation.

## Contribution

The novel contribution is a 3D LiDAR mapping framework using multivariate bundle adjustment cost factors for consistent global and local optimization.

## Key findings

- The proposed framework achieves accurate trajectory estimation.
- Experiments show consistent mapping results on public and self-collected LiDAR datasets.

## Abstract

Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrate that the proposed method achieves accurate trajectory estimation and consistent mapping.