紧耦合 LiDAR-视觉-惯性 SLAM 与大规模体积占据建图
Tightly-Coupled LiDAR-Visual-Inertial SLAM and Large-Scale Volumetric Occupancy Mapping
- Technical University of Munich（慕尼黑工业大学）
- Imperial College London（伦敦帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出紧耦合 LiDAR-视觉-惯性 SLAM 与三维占据建图框架，利用基于占据场梯度的无对应概率残差和局部子图因子图优化，实现高精度位姿估计与全局一致的大规模建图。
AI中文摘要：
自主导航是移动机器人在现实世界中每一项潜在应用的关键需求之一。除高精度状态估计外，一个合适且全局一致的三维环境表示也是不可或缺的。我们提出了一种完全紧耦合的 LiDAR-视觉-惯性 SLAM 系统和三维建图框架，采用局部子图策略以实现对大规模环境的可扩展性。文中引入了一种新颖的、无需对应关系且本质上具有概率性的 LiDAR 残差表述，该表述仅以占据场及其相应梯度表示。这些残差可加入因子图优化问题：既可作为实时估计的帧到图因子，也可作为使各子图彼此对齐的图到图因子。实验验证表明，该方法达到了最先进的位姿精度，并且生成了全局一致的体积占据子图，可直接用于导航或探索等下游任务。
英文摘要：
Autonomous navigation is one of the key requirements for every potential application of mobile robots in the real-world. Besides high-accuracy state estimation, a suitable and globally consistent representation of the 3D environment is indispensable. We present a fully tightly-coupled LiDAR-Visual-Inertial SLAM system and 3D mapping framework applying local submapping strategies to achieve scalability to large-scale environments. A novel and correspondence-free, inherently probabilistic, formulation of LiDAR residuals is introduced, expressed only in terms of the occupancy fields and its respective gradients. These residuals can be added to a factor graph optimisation problem, either as frame-to-map factors for the live estimates or as map-to-map factors aligning the submaps with respect to one another. Experimental validation demonstrates that the approach achieves state-of-the-art pose accuracy and furthermore produces globally consistent volumetric occupancy submaps which can be directly used in downstream tasks such as navigation or exploration.
