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

2025-01-07

AI Summary

Existing LiDAR SLAM methods suffer from limited robustness in joint pose estimation and scene reconstruction—particularly under degenerate geometries and low-quality point clouds. To address this, we propose a surfel-based LiDAR Bundle Adjustment (LiDAR BA) framework. This work introduces surfels into LiDAR BA for the first time, enabling scene-adaptive generalized uncertainty modeling that jointly encodes geometric degeneracy and measurement confidence. Our method integrates probabilistic residual weighting, point-cloud geometric consistency constraints, and Ceres-based nonlinear optimization to achieve end-to-end joint refinement of poses and surfel maps. Evaluated on multiple public benchmarks, the framework significantly improves localization accuracy and mapping robustness across challenging scenarios. Furthermore, the open-source implementation supports real-time execution on embedded platforms.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Learning to Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 representations like signed distance functions or neural networks. 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 degraded or 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.

Problem

• LiDAR systems
• sensor positioning
• image refinement

Innovation

• 3D LiDAR Beam Adjustment
• Uncertainty Modeling
• Open-source System