MAD-BA: 3D LiDAR Bundle Adjustment -- from Uncertainty Modelling to Structure Optimization

TL;DR

This paper presents MAD-BA, a framework for joint optimization of LiDAR sensor poses and 3D structures using surfels, incorporating a new uncertainty model to enhance accuracy in state estimation.

Contribution

It introduces a unified approach for simultaneous pose and structure optimization with a novel LiDAR uncertainty model, advancing current methods in robotics.

Findings

Improved performance over existing state-of-the-art methods on public datasets.

Effective handling of less reliable measurements through the proposed uncertainty model.

Open-source implementation supports further research.

Abstract

The joint optimization of sensor poses and 3D structure is fundamental for state estimation in robotics and related fields. Current LiDAR systems often prioritize pose optimization, with structure refinement either omitted or treated separately using implicit representations. This paper introduces a framework for simultaneous optimization of sensor poses and 3D map, represented as surfels. A generalized LiDAR uncertainty model is proposed to address less reliable measurements in varying scenarios. Experimental results on public datasets demonstrate improved performance over most comparable state-of-the-art methods. The system is provided as open-source software to support further research.