Large-scale SLAM remains challenging due to accumulated trajectory drift and the increasing computational cost of maintaining global consistency. Submap joining alleviates these issues by constructing locally consistent submaps and subsequently fusing them into a global map. However, existing occupancy-based submap joining methods operate on discrete grids, resulting in non-smooth gradients during optimization and neglecting the uncertainty associated with occupancy estimates. We propose the first continuous probabilistic submap joining framework that jointly optimizes submap poses and a global occupancy field in the latent log-odds space. The framework employs an information-preserving sparse Bayesian formulation that compresses raw occupancy observations into sufficient-statistic log-odds tuples while retaining the posterior information of the original observations. This yields closed-form predictive mean and variance estimates for occupancy mapping, which directly enable a submap joining formulation with analytical Jacobians, leading to more accurate submap joining and yielding a closed-form optimal global map upon pose convergence. Experiments on both simulated and large-scale real-world datasets demonstrate that the proposed method achieves higher pose accuracy and improved global consistency than state-of-the-art grid-based submap joining approaches, while producing more compact map representations and better-calibrated uncertainty estimates than existing continuous occupancy mapping methods.

翻译：大规模SLAM由于累积轨迹漂移和维护全局一致性的计算成本增加而仍然具有挑战性。子图拼接通过构建局部一致的子图并随后将其融合为全局地图来缓解这些问题。然而，现有的基于占据栅格的子图拼接方法操作在离散网格上，导致优化过程中梯度不光滑，且忽视了占据估计的不确定性。我们提出了首个连续概率子图拼接框架，该框架在潜在对数几率空间中联合优化子图位姿与全局占据场。该框架采用信息保持的稀疏贝叶斯公式，将原始占据观测压缩为充分统计量的对数几率元组，同时保留原始观测的后验信息。这为占据建图提供了闭式预测均值与方差估计，从而直接实现了具有解析雅可比矩阵的子图拼接公式，进而获得更精确的子图拼接结果，并在位姿收敛时生成闭式最优全局地图。在仿真与大规模真实世界数据集上的实验表明，所提方法相较于最先进的基于栅格的子图拼接方法实现了更高的位姿精度与改进的全局一致性，同时相较于现有连续占据建图方法生成了更紧凑的地图表示与更优校准的不确定性估计。