同时优化激光雷达位姿与三维结构，提升定位精度
MAD-BA: 3D LiDAR Bundle Adjustment -- from Uncertainty Modelling to Structure Optimization
- 用表面元（surfels）表示三维地图，联合优化位姿与结构
- 提出广义不确定性模型，适应不同场景下的测量可靠性
- 在公开数据集上超越多数先进方法，代码开源
机器人状态估计中，传感器位姿与三维结构的联合优化至关重要。当前激光雷达系统多侧重位姿优化，结构精修常被忽略或采用隐式表示。本文提出一种新框架，通过表面元（surfels）表示三维地图，实现传感器位姿与3D地图的联合优化。引入广义激光雷达不确定性模型，有效应对不同场景下测量可靠性差异。在多个公开数据集上的实验表明，该方法性能优于多数现有先进方法。系统已作为开源软件发布，支持后续研究。
原文摘要 · Abstract (English)
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.
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