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.

翻译：传感器位姿与三维结构的联合优化是机器人学及相关领域中状态估计的基础。当前激光雷达系统通常优先优化位姿，而结构优化要么被忽略，要么通过符号距离函数或神经网络等表示方法单独处理。本文提出了一种同时优化传感器位姿与三维地图（以面元表示）的框架。为应对不同场景下测量值退化或可靠性降低的问题，提出了一种广义激光雷达不确定性模型。在公开数据集上的实验结果表明，其性能优于大多数同类先进方法。该系统已作为开源软件发布，以支持进一步研究。